--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#     NOTE
##  Due to the data protection rules of the IAB, observations <= 100 were replaced by "NA". 
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  \\iab.baintern.de\DFS\017\Ablagen\D01700-Projekte\D01700-COAL\log/5estimations_2_20221212.log
  log type:  text
 opened on:  12 Dec 2022, 14:43:35
r; t=0.01 14:43:35
.         }
r; t=0.01 14:43:35

. *** Note which sample ***
. di "NB. this log-file reporting results for sample number: " ${sample}
NB. this log-file reporting results for sample number: 2
r; t=0.00 14:43:35

.         
.         
. *** OPEN DATA FILE ***
. * sample global takes values (1), (2), (3), (4) - see data doc for information on different samples
. * sample global value (7-9) is for JAERE revision, uses sample sample 2 but  
. * using only post-2000 data (sample==(2)) apart from that
. if ${sample}<7{
. use ${data}\postprep_${sample}.dta, clear
r; t=17.42 14:43:52
. }
r; t=17.42 14:43:52

. if ${sample}>=7{
. use ${data}\postprep_2.dta, clear
r; t=0.00 14:43:52
. }
r; t=0.00 14:43:52

. 
. * use same varnames in IAB and test data
.  if ${iab}==1{      
.               cap rename wz08_kons_num wz08
r; t=0.04 14:43:52
.                           * g pid=persnr
.               }
r; t=0.05 14:43:52

. 
. * The command "put excel close" does not work on LH version of Stata
. if $iab==1{
.         local putexcelclose="putexcel close"
r; t=0.00 14:43:52
. else 
r; t=0.00 14:43:52
.         local putexcelclose=""
r; t=0.00 14:43:52
.         }
r; t=0.01 14:43:52

. 
. /* ------------------------------------------------------------------------ */
.  *      NEW : KEEP ONLY WHEN 18 Years old or older
. /* ------------------------------------------------------------------------ */
. /***** Recalculate age at the end of spell ******/
. tab ageend, m           

     ageend |      Freq.     Percent        Cum.
------------+-----------------------------------
         13 |         NA        0.00        0.00
         14 |        173        0.01        0.02
         15 |      1,439        0.10        0.11
         16 |      8,528        0.57        0.69
         17 |     17,718        1.19        1.88
         18 |     21,472        1.45        3.32
         19 |     27,548        1.85        5.18
         20 |     32,005        2.15        7.33
         21 |     30,450        2.05        9.38
         22 |     25,348        1.71       11.09
         23 |     22,875        1.54       12.63
         24 |     21,530        1.45       14.08
         25 |     21,373        1.44       15.52
         26 |     21,545        1.45       16.97
         27 |     22,262        1.50       18.46
         28 |     22,540        1.52       19.98
         29 |     22,995        1.55       21.53
         30 |     23,718        1.60       23.13
         31 |     24,347        1.64       24.76
         32 |     25,322        1.70       26.47
         33 |     26,103        1.76       28.23
         34 |     26,858        1.81       30.03
         35 |     27,630        1.86       31.89
         36 |     28,198        1.90       33.79
         37 |     28,561        1.92       35.71
         38 |     29,009        1.95       37.67
         39 |     29,231        1.97       39.63
         40 |     29,893        2.01       41.65
         41 |     30,260        2.04       43.68
         42 |     30,470        2.05       45.73
         43 |     30,901        2.08       47.81
         44 |     30,755        2.07       49.88
         45 |     30,663        2.06       51.95
         46 |     30,237        2.04       53.98
         47 |     29,509        1.99       55.97
         48 |     29,521        1.99       57.96
         49 |     29,435        1.98       59.94
         50 |     29,701        2.00       61.94
         51 |     30,676        2.06       64.00
         52 |     30,352        2.04       66.05
         53 |     30,590        2.06       68.10
         54 |     31,898        2.15       70.25
         55 |     38,655        2.60       72.85
         56 |     30,375        2.04       74.90
         57 |     27,102        1.82       76.72
         58 |     24,989        1.68       78.40
         59 |     20,136        1.36       79.76
         60 |     38,551        2.59       82.35
         61 |     13,862        0.93       83.29
         62 |      9,994        0.67       83.96
         63 |     11,554        0.78       84.74
         64 |      4,460        0.30       85.04
         65 |      4,044        0.27       85.31
         66 |      1,952        0.13       85.44
         67 |      1,376        0.09       85.53
         68 |      1,077        0.07       85.61
         69 |        850        0.06       85.66
         70 |        722        0.05       85.71
         71 |        532        0.04       85.75
         72 |        454        0.03       85.78
         73 |        386        0.03       85.80
         74 |        274        0.02       85.82
         75 |        206        0.01       85.84
         76 |        363        0.02       85.86
          . |    210,047       14.14      100.00
------------+-----------------------------------
      Total |  1,485,653      100.00
r; t=0.42 14:43:53

. gen jahrend = year(endepi)
r; t=0.06 14:43:53

. label var jahrend "year at end of spell"
r; t=0.00 14:43:53

. replace ageend = jahrend - year(geb_dat)
(210,047 real changes made)
r; t=0.39 14:43:53

. label var ageend "age at end of spell"
r; t=0.00 14:43:53

. di "tab age at end of last spell of sampled workers"
tab age at end of last spell of sampled workers
r; t=0.00 14:43:53

. tab ageend, m

 age at end |
   of spell |      Freq.     Percent        Cum.
------------+-----------------------------------
         13 |         NA        0.00        0.00
         14 |        193        0.01        0.02
         15 |      1,540        0.10        0.12
         16 |      9,023        0.61        0.73
         17 |     18,804        1.27        1.99
         18 |     22,969        1.55        3.54
         19 |     29,632        1.99        5.53
         20 |     35,408        2.38        7.92
         21 |     36,593        2.46       10.38
         22 |     31,577        2.13       12.51
         23 |     28,249        1.90       14.41
         24 |     26,305        1.77       16.18
         25 |     26,111        1.76       17.94
         26 |     26,195        1.76       19.70
         27 |     26,556        1.79       21.49
         28 |     26,634        1.79       23.28
         29 |     27,093        1.82       25.10
         30 |     27,795        1.87       26.97
         31 |     28,155        1.90       28.87
         32 |     29,260        1.97       30.84
         33 |     30,086        2.03       32.86
         34 |     30,711        2.07       34.93
         35 |     31,555        2.12       37.05
         36 |     32,198        2.17       39.22
         37 |     32,570        2.19       41.41
         38 |     33,125        2.23       43.64
         39 |     33,424        2.25       45.89
         40 |     34,119        2.30       48.19
         41 |     34,538        2.32       50.51
         42 |     34,863        2.35       52.86
         43 |     35,273        2.37       55.24
         44 |     35,217        2.37       57.61
         45 |     35,120        2.36       59.97
         46 |     34,783        2.34       62.31
         47 |     34,057        2.29       64.60
         48 |     34,316        2.31       66.91
         49 |     34,161        2.30       69.21
         50 |     34,610        2.33       71.54
         51 |     35,857        2.41       73.96
         52 |     35,560        2.39       76.35
         53 |     35,920        2.42       78.77
         54 |     37,328        2.51       81.28
         55 |     44,084        2.97       84.25
         56 |     35,760        2.41       86.65
         57 |     32,319        2.18       88.83
         58 |     30,505        2.05       90.88
         59 |     25,336        1.71       92.59
         60 |     42,466        2.86       95.45
         61 |     16,880        1.14       96.58
         62 |     12,494        0.84       97.42
         63 |     13,747        0.93       98.35
         64 |      6,197        0.42       98.77
         65 |      5,394        0.36       99.13
         66 |      3,071        0.21       99.34
         67 |      2,264        0.15       99.49
         68 |      1,740        0.12       99.61
         69 |      1,433        0.10       99.70
         70 |      1,114        0.07       99.78
         71 |        863        0.06       99.84
         72 |        722        0.05       99.88
         73 |        594        0.04       99.92
         74 |        414        0.03       99.95
         75 |        312        0.02       99.97
         76 |        406        0.03      100.00
------------+-----------------------------------
      Total |  1,485,653      100.00
r; t=0.50 14:43:54

. 
. keep if ageend >= 18 
(29,615 observations deleted)
r; t=0.71 14:43:54

. 
. 
. /* ------------------------------------------------------------------------ */
.  * (00) Define additional variables for characteristics
. /* ------------------------------------------------------------------------ */  
.         /*      * mining_area_bin       binary indicator for mining_area (east/west)
>                 * educ2                         education categories                                                            
>                 * agecat2                       broad age categories                    */
. 
.         * Binary mining area indicator (east / west)    
.                         gen mining_area_bin = .
(1,456,038 missing values generated)
r; t=0.04 14:43:54

. 
.                         replace mining_area_bin = 1 if mining_area == 1 //Lausitzer Revier
(295,563 real changes made)
r; t=0.04 14:43:55

.                         replace mining_area_bin = 1 if mining_area == 2 //Mitteldeutsches Revier
(201,173 real changes made)
r; t=0.05 14:43:55

.                         replace mining_area_bin = 1 if ao_kreis == 03153 // LK Goslar                           // 75,067
(666 real changes made)
r; t=0.03 14:43:55

.                         replace mining_area_bin = 1 if ao_kreis == 13073 // LK Vorpommern-RÃƒÂ¼gen          // 20,402
(306 real changes made)
r; t=0.03 14:43:55

.                         replace mining_area_bin = 1 if ao_kreis == 14521 // LK Erzgebirgskreis          // 59,263
(836 real changes made)
r; t=0.04 14:43:55

.                         replace mining_area_bin = 1 if ao_kreis == 11000 // Stadt Berlin                        // 158,323
(11,705 real changes made)
r; t=0.03 14:43:55

.                         
.                         replace mining_area_bin = 2 if mining_area == 3 //Helmstedter Revier
(23,330 real changes made)
r; t=0.04 14:43:55

.                         replace mining_area_bin = 2 if mining_area == 4 //Rheinisches Revier
(178,806 real changes made)
r; t=0.04 14:43:55

.                         replace mining_area_bin = 2 if ao_kreis == 03241 // Region Hannover                     // 372,694
(4,403 real changes made)
r; t=0.05 14:43:55

.                         replace mining_area_bin = 2 if ao_kreis == 05570 // LK Warendorf                        // 189,594
(320 real changes made)
r; t=0.04 14:43:55

.                         replace mining_area_bin = 2 if ao_kreis == 06440 // LK Wetteraukreis            // 49,629
(4,279 real changes made)
r; t=0.04 14:43:55

.                         replace mining_area_bin = 2 if ao_kreis == 06611 // Stadt Kassel                        // 106,379
(1,479 real changes made)
r; t=0.04 14:43:55

.                         replace mining_area_bin = 2 if ao_kreis == 06634 // LK Schwalm-Eder-Kreis       // 77,271
(4,537 real changes made)
r; t=0.04 14:43:55

.                         replace mining_area_bin = 2 if ao_kreis == 06636 // LK Werra-MeiÃƒÅ¸ner-Kreis       // 41,551
(1,563 real changes made)
r; t=0.04 14:43:55

.                         replace mining_area_bin = 2 if ao_kreis == 09162 // Stadt MÃƒÂ¼nchen                        // 155,443
(5,173 real changes made)
r; t=0.04 14:43:55

.                         replace mining_area_bin = 2 if ao_kreis == 09376 // LK Schwandorf                       // 68,832
(10,052 real changes made)
r; t=0.03 14:43:55

.                         replace mining_area_bin = 2 if ao_kreis == 09672 // LK Bad Kissingen            // 12,181
(129 real changes made)
r; t=0.04 14:43:55

.                         replace mining_area_bin = 2 if ao_kreis == 10041 // Regionalverband SaarbrÃƒÂ¼cken  // 758,671
(3,024 real changes made)
r; t=0.04 14:43:55

.                         replace mining_area_bin = 2 if ao_kreis == 10044 // LK Saarlouis                        // 245,729
(586 real changes made)
r; t=0.04 14:43:55

. 
.                         label define areas_bin 1 "East" 2 "West"
r; t=0.00 14:43:55

.                         label variable mining_area_bin "Mining Area East(1) / West(2)"
r; t=0.00 14:43:55

.                         
.                         tab mining_area_bin, m

Mining Area |
  East(1) / |
    West(2) |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |    510,249       35.04       35.04
          2 |    237,681       16.32       51.37
          . |    708,108       48.63      100.00
------------+-----------------------------------
      Total |  1,456,038      100.00
r; t=0.26 14:43:56

.                                 
.         * Education: use variable bild created in 1prepare and aggregate into two categories
.                         tab bild, m

            Education, imputed based on |
      Fitzenberger, Osikominu & Voelter |
                                 (2008) |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
               1 no vocational training |    183,452       12.60       12.60
                  2 vocational training |  1,128,651       77.52       90.11
3 university or university of applied s |    117,696        8.08       98.20
                                      . |     26,239        1.80      100.00
----------------------------------------+-----------------------------------
                                  Total |  1,456,038      100.00
r; t=0.23 14:43:56

.                         cap drop educ2
r; t=0.00 14:43:56

.                         gen educ2=. 
(1,456,038 missing values generated)
r; t=0.04 14:43:56

.                         replace educ2=0 if bild == 1 // neither vocational training or degree from universtiy
(183,452 real changes made)
r; t=0.04 14:43:56

.                         replace educ2=1 if bild == 2 | bild == 3 // vocational training (Ausbildung) or degree from an university or university of applied science (Uni or FH)
(1,246,347 real changes made)
r; t=0.07 14:43:56

.                         label define educLAB 0 "keine abg. Ausbild." 1 "abg. Ausbildung" 
r; t=0.00 14:43:56

.                         label values educ2 educLAB
r; t=0.00 14:43:56

.                         label variable educ2 "education 2 categories"
r; t=0.00 14:43:56

. 
.         * Age quantiles: Define age categories manually 
.                         cap drop agecat2        
r; t=0.00 14:43:56

.                         gen agecat2 = 1 if ageend >= 18 & ageend <= 30
(1,084,921 missing values generated)
r; t=0.05 14:43:56

.                         replace agecat2 = 2 if ageend > 30 & ageend <= 49
(627,531 real changes made)
r; t=0.05 14:43:56

.                         replace agecat2 = 3 if ageend > 49 & ageend !=. 
(457,390 real changes made)
r; t=0.05 14:43:56

.                         label var agecat2 "age at end of spell by category"
r; t=0.00 14:43:56

.                         label define agecat2LAB 1 "age18-30" 2 "age31-49" 3 "age50+" 
r; t=0.00 14:43:56

.                         label values agecat2 agecat2LAB
r; t=0.00 14:43:56

.         * Decade of end of spell (decade 1 are actually more than two decades)
.                 cap drop decades
r; t=0.00 14:43:56

.                 g decades=.
(1,456,038 missing values generated)
r; t=0.04 14:43:56

.                 replace decades=1 if endepi>=mdy(01,01,1970) & endepi<mdy(01,01,1992)
(169,119 real changes made)
r; t=0.08 14:43:56

.                 * note post-92 (incl. East Germany)
.                 replace decades=2 if endepi>=mdy(01,01,1992) & endepi<mdy(01,01,2000)
(511,389 real changes made)
r; t=0.08 14:43:56

.                 * JAERE: if we wish to exclude pre-2000 data, include only decades 3&4
.                 replace decades=3 if endepi>=mdy(01,01,2000) & endepi<mdy(01,01,2010)
(477,313 real changes made)
r; t=0.08 14:43:56

.                 replace decades=4 if endepi>=mdy(01,01,2010) & endepi!=.
(298,215 real changes made)
r; t=0.06 14:43:56

.                 
.                 label var decades "decade in which spell ended"
r; t=0.00 14:43:56

.                 label define decadesLAB 1 "1970s-1980s" 2 "1990s" 3 "2000s" 4 "2010s"
r; t=0.00 14:43:56

.                 label values decades decadesLAB
r; t=0.00 14:43:56

.                 tab decade, m

     decade |      Freq.     Percent        Cum.
------------+-----------------------------------
          6 |         NA        0.00        0.00
          7 |     72,018        4.95        4.95
          8 |     69,730        4.79        9.74
          9 |    528,339       36.29       46.02
         10 |    395,481       27.16       73.18
         11 |    182,124       12.51       85.69
          . |    208,343       14.31      100.00
------------+-----------------------------------
      Total |  1,456,038      100.00
r; t=0.24 14:43:57

. 
. 
. 
. /* ------------------------------------------------------------------------ */
.  *      (0.a) Investigate black holes and ATZ case (person103==1)
. /* ------------------------------------------------------------------------ */
.         
.         *Black holes
.         * How black holes (status==10) are treated
.         tab status

                      status |      Freq.     Percent        Cum.
-----------------------------+-----------------------------------
                  unemployed |    348,165       23.91       23.91
            active_labour_mp |     15,355        1.05       24.97
         marginal_employment |     54,888        3.77       28.74
normal_employment_(FT_or_PT) |    729,303       50.09       78.82
         vocational_training |     99,984        6.87       85.69
                          10 |    208,343       14.31      100.00
-----------------------------+-----------------------------------
                       Total |  1,456,038      100.00
r; t=0.24 14:43:57

.         tab statsimple status, missing

       0 - |
unemployed |
 , margemp |
 or ALMP / |
       1 - |
employed / |
       2 - |                              status
vocational | unemploye  active_la  marginal_  normal_em  vocationa         10 |     Total
-----------+------------------------------------------------------------------+----------
         0 |   348,165     15,355     54,888          0          0          0 |   418,408 
         1 |         0          0          0    729,303          0          0 |   729,303 
         2 |         0          0          0          0     99,984          0 |    99,984 
         . |         0          0          0          0          0    208,343 |   208,343 
-----------+------------------------------------------------------------------+----------
     Total |   348,165     15,355     54,888    729,303     99,984    208,343 | 1,456,038 
r; t=0.36 14:43:57

.         
.         *Is it possible that first spell is a black hole
.         tab status if spell==1

                      status |      Freq.     Percent        Cum.
-----------------------------+-----------------------------------
                  unemployed |     10,073        7.69        7.69
            active_labour_mp |      1,086        0.83        8.51
         marginal_employment |      3,532        2.69       11.21
normal_employment_(FT_or_PT) |    107,468       81.99       93.20
         vocational_training |      8,911        6.80      100.00
-----------------------------+-----------------------------------
                       Total |    131,070      100.00
r; t=0.27 14:43:58

. 
.         *Is it possible that last spell is a black hole
.         cap drop last_spell
r; t=0.00 14:43:58

.         by pid (begepi), sort: gen byte last_spell = (_n == _N)
r; t=0.45 14:43:58

.         tab status if last_spell==1

                      status |      Freq.     Percent        Cum.
-----------------------------+-----------------------------------
                  unemployed |     54,906       37.37       37.37
            active_labour_mp |      1,365        0.93       38.30
         marginal_employment |     17,166       11.68       49.99
normal_employment_(FT_or_PT) |     72,476       49.33       99.32
         vocational_training |      1,003        0.68      100.00
-----------------------------+-----------------------------------
                       Total |    146,916      100.00
r; t=0.24 14:43:58

.         
.         * What is before?
.         tab status if status[_n+1]==10 & pid==pid[_n+1]

                      status |      Freq.     Percent        Cum.
-----------------------------+-----------------------------------
                  unemployed |     63,295       30.51       30.51
            active_labour_mp |      2,701        1.30       31.81
         marginal_employment |     23,487       11.32       43.13
normal_employment_(FT_or_PT) |    112,934       54.43       97.56
         vocational_training |      5,046        2.43      100.00
                          10 |         NA        0.00      100.00
-----------------------------+-----------------------------------
                       Total |    207,472      100.00
r; t=0.51 14:43:59

.         
.         * What is after?
.         tab status if status[_n-1]==10 & pid==pid[_n-1]

                      status |      Freq.     Percent        Cum.
-----------------------------+-----------------------------------
                  unemployed |     76,007       36.48       36.48
            active_labour_mp |      2,829        1.36       37.84
         marginal_employment |     32,013       15.37       53.21
normal_employment_(FT_or_PT) |     93,068       44.67       97.88
         vocational_training |      4,417        2.12      100.00
                          10 |         NA        0.00      100.00
-----------------------------+-----------------------------------
                       Total |    208,343      100.00
r; t=0.41 14:43:59

. 
.         *How many persons are concerned by black holes
.         cap drop nvals 
r; t=0.00 14:43:59

.         gen nobs_blackhole=0
r; t=0.05 14:43:59

.         by pid status, sort: replace nobs_blackhole = _n == 1 if status==10
(89025 real changes made)
r; t=0.72 14:44:00

.         tab nobs_blackhole, missing     

nobs_blackh |
        ole |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  1,367,013       93.89       93.89
          1 |     89,025        6.11      100.00
------------+-----------------------------------
      Total |  1,456,038      100.00
r; t=0.25 14:44:00

.         
.         *ATZ cases
.         count if endepi<begepi & person103!=1
  0
r; t=0.05 14:44:00

.         count if endepi<begepi & person103==1
  0
r; t=0.05 14:44:00

.         tab statsimple Dlast103

       0 - |
unemployed |
 , margemp |      dummy last
 or ALMP / |    ATZ-observation
       1 - |  unless last date is
employed / |  post-Sept2017 (last
       2 - |         wave)
vocational |         0          1 |     Total
-----------+----------------------+----------
         0 |   418,382         22 |   418,404 
         1 |   727,179         79 |   727,258 
         2 |    99,984          0 |    99,984 
-----------+----------------------+----------
     Total | 1,245,545        101 | 1,245,646 
r; t=0.34 14:44:01

.         tab statsimple Dfirst103

       0 - |
unemployed |
 , margemp |
 or ALMP / |
       1 - |
employed / |      dummy first
       2 - |    ATZ-observation
vocational |         0          1 |     Total
-----------+----------------------+----------
         0 |   418,407          1 |   418,408 
         1 |   724,200      5,103 |   729,303 
         2 |    99,984          0 |    99,984 
-----------+----------------------+----------
     Total | 1,242,591      5,104 | 1,247,695 
r; t=0.34 14:44:01

.         tab statsimple if mid103>begepi & mid103<endepi

        0 - |
unemployed, |
 margemp or |
 ALMP / 1 - |
 employed / |
        2 - |
 vocational |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         NA        0.24        0.24
          1 |      4,159       99.76      100.00
------------+-----------------------------------
      Total |      4,169      100.00
r; t=0.39 14:44:01

.         
. /* ------------------------------------------------------------------------ */  
.  * (0.b) Investigate difference between sample size of potret & sample size of people retiring  
. /* ------------------------------------------------------------------------ */
.         
.         di "over 49"
over 49
r; t=0.00 14:44:01

.                 tab ageend if dpotret==1

 age at end |
   of spell |      Freq.     Percent        Cum.
------------+-----------------------------------
         18 |         NA        0.03        0.03
         19 |         NA        0.07        0.10
         20 |        176        0.16        0.26
         21 |        232        0.21        0.47
         22 |        217        0.19        0.66
         23 |        213        0.19        0.85
         24 |        237        0.21        1.06
         25 |        245        0.22        1.28
         26 |        243        0.22        1.50
         27 |        272        0.24        1.74
         28 |        286        0.26        1.99
         29 |        310        0.28        2.27
         30 |        371        0.33        2.60
         31 |        374        0.33        2.94
         32 |        428        0.38        3.32
         33 |        450        0.40        3.72
         34 |        470        0.42        4.14
         35 |        496        0.44        4.58
         36 |        570        0.51        5.09
         37 |        597        0.53        5.62
         38 |        652        0.58        6.20
         39 |        714        0.64        6.84
         40 |        810        0.72        7.56
         41 |        882        0.79        8.35
         42 |        926        0.83        9.17
         43 |        998        0.89       10.06
         44 |      1,032        0.92       10.98
         45 |      1,212        1.08       12.06
         46 |      1,253        1.12       13.18
         47 |      1,411        1.26       14.44
         48 |      1,549        1.38       15.82
         49 |      1,706        1.52       17.34
         50 |      1,907        1.70       19.04
         51 |      2,162        1.93       20.97
         52 |      2,294        2.05       23.02
         53 |      2,668        2.38       25.39
         54 |      3,218        2.87       28.26
         55 |      4,131        3.68       31.95
         56 |      4,516        4.03       35.97
         57 |      5,080        4.53       40.50
         58 |      8,042        7.17       47.68
         59 |      7,667        6.84       54.51
         60 |     28,993       25.85       80.36
         61 |      6,431        5.73       86.10
         62 |      4,367        3.89       89.99
         63 |      7,128        6.36       96.35
         64 |      1,728        1.54       97.89
         65 |      1,805        1.61       99.50
         66 |        248        0.22       99.72
         67 |         NA        0.09       99.81
         68 |         NA        0.04       99.85
         69 |         NA        0.03       99.88
         70 |         NA        0.04       99.92
         71 |         NA        0.03       99.95
         72 |         NA        0.01       99.97
         73 |         NA        0.01       99.98
         74 |         NA        0.01       99.99
         75 |         NA        0.00       99.99
         76 |         NA        0.01      100.00
------------+-----------------------------------
      Total |    112,146      100.00
r; t=0.28 14:44:02

.                 tab agepotret if dpotret==1

  agepotret |      Freq.     Percent        Cum.
------------+-----------------------------------
         49 |      1,546        1.63        1.63
         50 |      1,975        2.08        3.70
         51 |      1,990        2.09        5.79
         52 |      2,191        2.30        8.10
         53 |      2,567        2.70       10.80
         54 |      2,811        2.95       13.75
         55 |      4,391        4.62       18.37
         56 |      4,108        4.32       22.68
         57 |      5,343        5.62       28.30
         58 |      8,657        9.10       37.40
         59 |      7,927        8.33       45.73
         60 |     33,573       35.29       81.03
         61 |      4,576        4.81       85.84
         62 |      3,184        3.35       89.18
         63 |      6,912        7.27       96.45
         64 |      1,303        1.37       97.82
         65 |      1,698        1.78       99.61
         66 |        105        0.11       99.72
         67 |         NA        0.07       99.79
         68 |         NA        0.04       99.83
         69 |         NA        0.05       99.87
         70 |         NA        0.05       99.92
         71 |         NA        0.03       99.95
         72 |         NA        0.02       99.97
         73 |         NA        0.01       99.98
         74 |         NA        0.01       99.99
         75 |         NA        0.01       99.99
         76 |         NA        0.01      100.00
------------+-----------------------------------
      Total |     95,127      100.00
r; t=0.27 14:44:02

.                 count if agepotret==ageend
  70,051
r; t=0.06 14:44:02

.                 count if agepotret<ageend
  54,722
r; t=0.07 14:44:02

.                 count if agepotret>ageend
  1,331,265
r; t=0.06 14:44:02

.                 tab agepotret ageend if dpotret==1 & agepotret!=ageend & agepotret>49

NA

.         di "grund 130"
grund 130
r; t=0.00 14:44:03

.                 tab grund if dpotret==1

                              Abm grund |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                        -7 Keine Angabe |      3,533        3.15        3.15
                              -5 (leer) |      1,980        1.77        4.92
                                     10 |     25,843       23.04       27.96
         130 Abm. wg. Beschäftigungsend |     19,767       17.63       45.59
             131 Abm. wg. Kassenwechsel |        217        0.19       45.78
         132 Abm. wg. Beitragsgr.wechse |        394        0.35       46.13
                      133 Abm. sonstige |        636        0.57       46.70
         134 Abm. wg. § 7 Abs. 3 Satz 1 |        128        0.11       46.81
         136 Abm. wg. Entgelt/Whrgs.wec |         NA        0.04       46.85
           140 Anm./Abm. wg. Besch.ende |         NA        0.08       46.93
                       149 Abm. wg. Tod |      2,234        1.99       48.93
                      150 Jahresmeldung |      3,012        2.69       51.61
         151 Ubr.m. wg. Entgeltersatzls |      5,910        5.27       56.88
         152 Ubr.m. wg. Ehrziehungsurla |         NA        0.02       56.90
         153 Ubr.m. wg. gesetzl. Dienst |         NA        0.01       56.91
                           155 Störfall |         NA        0.09       56.99
         156 M. Untersch.btr. EGEL wh.  |         NA        0.01       57.01
         157 Gesonderte Meld. nach § 19 |        107        0.10       57.10
         159 Entgeltmldg. f. unst. Besc |         NA        0.00       57.10
         172 Entgeltmldg. z. Besch.ende |         NA        0.01       57.11
            229 Vermittlung ohne Hilfen |         NA        0.01       57.12
         230 Verm. in kurzfr. Beschäfti |         NA        0.00       57.12
          233 Vermittl. mit EGZ mit BHI |         NA        0.00       57.12
         234 Einstell.zuschuss Neugründ |         NA        0.00       57.12
          236 Verm. in Struk.anpass.maß |         NA        0.00       57.12
         237 selbst gesucht (über 7 Tag |         NA        0.06       57.18
         238 selbst gesucht (Beschäftig |         NA        0.00       57.19
         240 Maßn. berufl. Eingl. Behin |         NA        0.02       57.20
                 241 Arbeitsunfähigkeit |        847        0.76       57.96
          242 Rückruf/Wiedereinstellung |         NA        0.00       57.96
            243 betriebliche Ausbildung |         NA        0.00       57.96
                244 fehlende Mitwirkung |        660        0.59       58.55
                     245 Wohnortwechsel |         NA        0.02       58.57
         246 selbst. Tätigkeit ohne ÜBG |         NA        0.05       58.63
         247 ÜBG bei Aufn. selbst.Tätig |         NA        0.05       58.67
              249 Schulbesuch / Studium |         NA        0.00       58.68
                   250 Sonderregelungen |         NA        0.07       58.75
         251 vorüb. Wegfall  Voraussetz |         NA        0.01       58.75
         252 Ausscheiden aus Erwerbsleb |      5,580        4.98       63.73
         253 Wehr- / Zivildienst Wehrüb |         NA        0.03       63.76
                    254 sonstige Gründe |        400        0.36       64.11
         255 Beendigung der Hilfebedürf |        407        0.36       64.48
         256 Verbleib akt. Beschäftigun |         NA        0.00       64.48
           260 durch BA / JC vermittelt |         NA        0.01       64.49
         261 Vermittlung in Bürgerarbei |         NA        0.00       64.49
         263 durch Dritte/priv. AV verm |         NA        0.00       64.50
         265 Wiedereinstellung gleicher |         NA        0.03       64.53
                     266 selbst gesucht |        132        0.12       64.65
                    267 Selbständigkeit |        159        0.14       64.79
         268 Wehr-/Freiwilligen-/Zivild |         NA        0.02       64.81
         269 Schule/Studium/schul. Beru |         NA        0.03       64.84
         270 (außer-)betriebliche Ausbi |         NA        0.00       64.84
         271 Geförderte berufliche Weit |         NA        0.00       64.84
         273 Nichterneuerung der Meldun |        490        0.44       65.28
         274 Fehlende Verfügbark./Mitwi |      1,947        1.74       67.01
                   275 Sonderregelungen |      2,590        2.31       69.32
         276 Ausscheiden aus Erwerbsleb |      3,768        3.36       72.68
         277 Umzug in anderen AA-Bezirk |         NA        0.01       72.69
                    278 sonstige Gründe |        293        0.26       72.95
         279 sonst.Maßn.d.aktiven Arbei |         NA        0.00       72.95
         281 Betreuung durch zugel. kom |         NA        0.08       73.03
         282 Erwerbstätigkeit ohne nähe |         NA        0.07       73.10
                283 sonstige Ausbildung |         NA        0.00       73.10
                   287 Nichtaktivierung |         NA        0.00       73.10
                 290 Vermittlungssperre |         NA        0.04       73.14
         291 Selbst- und Fremdförderung |         NA        0.03       73.16
                       297 Keine Angabe |         NA        0.01       73.17
         298 durch AG/Bewerber in Jobbö |         NA        0.00       73.18
         300 DS durch Splittung erzeugt |         NA        0.00       73.18
         301 Besch.(15 Std. n. ehreamt. |         NA        0.00       73.18
                   305 dto. - unbekannt |         NA        0.00       73.18
         306 Selbst. (15 Std. p. Woche, |         NA        0.00       73.18
         308 Arbeitsunfähigkeit (Krankh |         NA        0.00       73.18
                 309 Erwerbsunfähigkeit |         NA        0.05       73.23
         312 a.marktpol. Maßn./s.Förd.  |         NA        0.00       73.23
                           320 sonstige |        137        0.12       73.35
         326 fehlende Mitwirkung / Verf |         NA        0.01       73.36
         327 dto. - d. zkT verm. - n- e |         NA        0.00       73.36
         328 Beendigung der Hilfebedürf |        191        0.17       73.53
         329 Betreuung durch einen ande |         NA        0.02       73.55
         330 Sonderregelung nach § 53 a |         NA        0.00       73.55
         399 DS durch Splittung erzeugt |         NA        0.00       73.55
             403 gesundheitliche Gründe |         NA        0.00       73.55
                    404 sonstige Gründe |         NA        0.01       73.56
         405 Maßn. ohne Prüfung und kei |         NA        0.01       73.57
                  420 verhaltensbedingt |         NA        0.00       73.57
         429 Ende SGBII-Bez. o. Aufn. A |         NA        0.00       73.57
         431 MNziel nicht err. (o. gesu |         NA        0.00       73.57
         433 Maßnahmeziel wurde nicht e |         NA        0.00       73.57
         434 Maßnahmeziel wurde erreich |         NA        0.00       73.58
                    501 Arbeitsaufnahme |         NA        0.01       73.59
                      504 Übergangsgeld |         NA        0.00       73.59
                              506 Umzug |         NA        0.03       73.62
                   507 eigene Abmeldung |         NA        0.03       73.64
          508 Ende Leistungsfortzahlung |         NA        0.01       73.66
                        509 Altersrente |        627        0.56       74.21
                    512 Sonstige Gründe |        642        0.57       74.79
          513 Vollendung 65. Lebensjahr |         NA        0.06       74.84
         514 Ablauf des Bewilligungszei |        168        0.15       74.99
         515 Wegfall der Erwerbsfähigke |        313        0.28       75.27
         516 Wegfall der Hilfebedürftig |        236        0.21       75.48
                   1101 Arbeitsaufnahme |        378        0.34       75.82
           1102 Erlöschen des Anspruchs |         NA        0.00       75.82
        1103 Leistungsempfänger verstor |         NA        0.04       75.86
        1105 Erwerbsminderungsrente <15 |        367        0.33       76.19
               1107 Ausreise ohne E 303 |         NA        0.03       76.22
        1113 Erwerbsminderungsrnt. 15-3 |         NA        0.08       76.30
                 1114 Mutterschaftsgeld |         NA        0.01       76.31
                   1115 Sonstige Gründe |      4,947        4.41       80.72
        1116 Ende Lfz/Anspruch Krankeng |        475        0.42       81.14
         1119 Bew. Abschnitt abgelaufen |        707        0.63       81.77
        1121 Erw.mind.rnt Leistverm. <1 |         NA        0.06       81.83
             1125 Abbruch der Massnahme |         NA        0.01       81.84
                1127 Anspruch erschöpft |      5,578        4.97       86.82
                       1132 Einstellung |         NA        0.07       86.89
            1135 Gesetzl. Dienstpflicht |         NA        0.03       86.92
                        1136 Wehrdienst |         NA        0.00       86.92
                   1137 Ortsabwesenheit |         NA        0.01       86.93
                    1139 65. Lebensjahr |        103        0.09       87.02
          1140 Berufausbildungsbeihilfe |         NA        0.00       87.02
        1141 Ende Lfz/Anspruch Krankeng |      1,769        1.58       88.60
            1143 Migration nach COLIBRI |         NA        0.00       88.60
           1144 Nichterscheinen Meldung |         NA        0.07       88.67
                     1147 Übergangsgeld |         NA        0.02       88.69
                1150 Studium Ausbildung |         NA        0.00       88.69
        1152 Erwm.rnt Leistverm. 15-30S |         NA        0.03       88.72
                             1153 Umzug |         NA        0.03       88.74
                       1154 Altersrente |     12,217       10.89       99.64
                    1155 Unterhaltsgeld |         NA        0.00       99.64
                     1156 Kuraufenthalt |        147        0.13       99.77
                1157 Ausreise mit E 303 |         NA        0.00       99.78
           1158 Sperrzeit 3/6/12 Wochen |         NA        0.00       99.78
              1159 Ablauf der Massnahme |         NA        0.02       99.80
                  1160 Eigene Abmeldung |        175        0.16       99.95
         1161 Wegfall der Verfügbarkeit |         NA        0.01       99.96
                1162 3. Meldeversäumnis |         NA        0.01       99.97
        1163 Aufn. einer selbst. Tätigk |         NA        0.00       99.97
        1165 Ende Arbeitssuche im Ausla |         NA        0.00       99.97
        1166 Ablauf des Mitnahmezeitrau |         NA        0.00       99.97
        1167 Reha-MN mit Anspruch auf Ü |         NA        0.02      100.00
        1168 Reha-MN ohne Anspruch auf  |         NA        0.00      100.00
----------------------------------------+-----------------------------------
                                  Total |    112,144      100.00
r; t=0.32 14:44:03

.                 tab grund if dpotret==1 & agepotret>49

                              Abm grund |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                        -7 Keine Angabe |      3,465        3.13        3.13
                              -5 (leer) |      1,930        1.75        4.88
                                     10 |     25,361       22.93       27.81
         130 Abm. wg. Beschäftigungsend |     19,591       17.71       45.52
             131 Abm. wg. Kassenwechsel |        214        0.19       45.72
         132 Abm. wg. Beitragsgr.wechse |        393        0.36       46.07
                      133 Abm. sonstige |        630        0.57       46.64
         134 Abm. wg. § 7 Abs. 3 Satz 1 |        126        0.11       46.75
         136 Abm. wg. Entgelt/Whrgs.wec |         NA        0.04       46.80
           140 Anm./Abm. wg. Besch.ende |         NA        0.08       46.88
                       149 Abm. wg. Tod |      2,148        1.94       48.82
                      150 Jahresmeldung |      2,957        2.67       51.49
         151 Ubr.m. wg. Entgeltersatzls |      5,714        5.17       56.66
         152 Ubr.m. wg. Ehrziehungsurla |         NA        0.02       56.67
         153 Ubr.m. wg. gesetzl. Dienst |         NA        0.01       56.68
                           155 Störfall |         NA        0.09       56.77
         156 M. Untersch.btr. EGEL wh.  |         NA        0.01       56.78
         157 Gesonderte Meld. nach § 19 |        107        0.10       56.88
         159 Entgeltmldg. f. unst. Besc |         NA        0.00       56.88
         172 Entgeltmldg. z. Besch.ende |         NA        0.01       56.89
            229 Vermittlung ohne Hilfen |         NA        0.01       56.90
         230 Verm. in kurzfr. Beschäfti |         NA        0.00       56.90
          233 Vermittl. mit EGZ mit BHI |         NA        0.00       56.90
         234 Einstell.zuschuss Neugründ |         NA        0.00       56.90
          236 Verm. in Struk.anpass.maß |         NA        0.00       56.90
         237 selbst gesucht (über 7 Tag |         NA        0.06       56.96
         238 selbst gesucht (Beschäftig |         NA        0.00       56.97
         240 Maßn. berufl. Eingl. Behin |         NA        0.02       56.98
                 241 Arbeitsunfähigkeit |        825        0.75       57.73
          242 Rückruf/Wiedereinstellung |         NA        0.00       57.73
            243 betriebliche Ausbildung |         NA        0.00       57.73
                244 fehlende Mitwirkung |        645        0.58       58.31
                     245 Wohnortwechsel |         NA        0.03       58.34
         246 selbst. Tätigkeit ohne ÜBG |         NA        0.05       58.39
         247 ÜBG bei Aufn. selbst.Tätig |         NA        0.05       58.44
              249 Schulbesuch / Studium |         NA        0.00       58.44
                   250 Sonderregelungen |         NA        0.07       58.51
         251 vorüb. Wegfall  Voraussetz |         NA        0.01       58.52
         252 Ausscheiden aus Erwerbsleb |      5,557        5.02       63.55
         253 Wehr- / Zivildienst Wehrüb |         NA        0.03       63.57
                    254 sonstige Gründe |        388        0.35       63.92
         255 Beendigung der Hilfebedürf |        388        0.35       64.27
         256 Verbleib akt. Beschäftigun |         NA        0.00       64.28
           260 durch BA / JC vermittelt |         NA        0.01       64.29
         261 Vermittlung in Bürgerarbei |         NA        0.00       64.29
         263 durch Dritte/priv. AV verm |         NA        0.00       64.30
         265 Wiedereinstellung gleicher |         NA        0.03       64.33
                     266 selbst gesucht |        127        0.11       64.44
                    267 Selbständigkeit |        153        0.14       64.58
         268 Wehr-/Freiwilligen-/Zivild |         NA        0.02       64.60
         269 Schule/Studium/schul. Beru |         NA        0.03       64.63
         270 (außer-)betriebliche Ausbi |         NA        0.00       64.63
         271 Geförderte berufliche Weit |         NA        0.00       64.63
         273 Nichterneuerung der Meldun |        478        0.43       65.07
         274 Fehlende Verfügbark./Mitwi |      1,914        1.73       66.80
                   275 Sonderregelungen |      2,571        2.32       69.12
         276 Ausscheiden aus Erwerbsleb |      3,758        3.40       72.52
         277 Umzug in anderen AA-Bezirk |         NA        0.01       72.52
                    278 sonstige Gründe |        285        0.26       72.78
         279 sonst.Maßn.d.aktiven Arbei |         NA        0.00       72.79
         281 Betreuung durch zugel. kom |         NA        0.08       72.86
         282 Erwerbstätigkeit ohne nähe |         NA        0.07       72.93
                283 sonstige Ausbildung |         NA        0.00       72.93
                   287 Nichtaktivierung |         NA        0.00       72.93
                 290 Vermittlungssperre |         NA        0.04       72.97
         291 Selbst- und Fremdförderung |         NA        0.03       73.00
                       297 Keine Angabe |         NA        0.01       73.00
         298 durch AG/Bewerber in Jobbö |         NA        0.00       73.01
         300 DS durch Splittung erzeugt |         NA        0.00       73.01
         301 Besch.(15 Std. n. ehreamt. |         NA        0.00       73.01
                   305 dto. - unbekannt |         NA        0.00       73.01
         306 Selbst. (15 Std. p. Woche, |         NA        0.00       73.01
         308 Arbeitsunfähigkeit (Krankh |         NA        0.00       73.01
                 309 Erwerbsunfähigkeit |         NA        0.05       73.06
         312 a.marktpol. Maßn./s.Förd.  |         NA        0.00       73.06
                           320 sonstige |        137        0.12       73.19
         326 fehlende Mitwirkung / Verf |         NA        0.01       73.19
         327 dto. - d. zkT verm. - n- e |         NA        0.00       73.20
         328 Beendigung der Hilfebedürf |        184        0.17       73.36
         329 Betreuung durch einen ande |         NA        0.02       73.38
         330 Sonderregelung nach § 53 a |         NA        0.00       73.38
         399 DS durch Splittung erzeugt |         NA        0.00       73.38
             403 gesundheitliche Gründe |         NA        0.00       73.38
                    404 sonstige Gründe |         NA        0.01       73.39
         405 Maßn. ohne Prüfung und kei |         NA        0.01       73.40
                  420 verhaltensbedingt |         NA        0.00       73.40
         429 Ende SGBII-Bez. o. Aufn. A |         NA        0.00       73.40
         431 MNziel nicht err. (o. gesu |         NA        0.00       73.40
         433 Maßnahmeziel wurde nicht e |         NA        0.00       73.40
         434 Maßnahmeziel wurde erreich |         NA        0.00       73.41
                    501 Arbeitsaufnahme |         NA        0.01       73.41
                      504 Übergangsgeld |         NA        0.00       73.42
                              506 Umzug |         NA        0.03       73.44
                   507 eigene Abmeldung |         NA        0.03       73.47
          508 Ende Leistungsfortzahlung |         NA        0.01       73.49
                        509 Altersrente |        627        0.57       74.05
                    512 Sonstige Gründe |        630        0.57       74.62
          513 Vollendung 65. Lebensjahr |         NA        0.06       74.68
         514 Ablauf des Bewilligungszei |        168        0.15       74.83
         515 Wegfall der Erwerbsfähigke |        302        0.27       75.11
         516 Wegfall der Hilfebedürftig |        231        0.21       75.31
                   1101 Arbeitsaufnahme |        373        0.34       75.65
           1102 Erlöschen des Anspruchs |         NA        0.00       75.65
        1103 Leistungsempfänger verstor |         NA        0.04       75.69
        1105 Erwerbsminderungsrente <15 |        355        0.32       76.01
               1107 Ausreise ohne E 303 |         NA        0.03       76.04
        1113 Erwerbsminderungsrnt. 15-3 |         NA        0.07       76.12
                 1114 Mutterschaftsgeld |         NA        0.01       76.13
                   1115 Sonstige Gründe |      4,875        4.41       80.53
        1116 Ende Lfz/Anspruch Krankeng |        463        0.42       80.95
         1119 Bew. Abschnitt abgelaufen |        703        0.64       81.59
        1121 Erw.mind.rnt Leistverm. <1 |         NA        0.06       81.65
             1125 Abbruch der Massnahme |         NA        0.01       81.66
                1127 Anspruch erschöpft |      5,557        5.02       86.68
                       1132 Einstellung |         NA        0.07       86.76
            1135 Gesetzl. Dienstpflicht |         NA        0.03       86.78
                        1136 Wehrdienst |         NA        0.00       86.78
                   1137 Ortsabwesenheit |         NA        0.01       86.80
                    1139 65. Lebensjahr |        103        0.09       86.89
          1140 Berufausbildungsbeihilfe |         NA        0.00       86.89
        1141 Ende Lfz/Anspruch Krankeng |      1,723        1.56       88.45
            1143 Migration nach COLIBRI |         NA        0.00       88.45
           1144 Nichterscheinen Meldung |         NA        0.07       88.52
                     1147 Übergangsgeld |         NA        0.02       88.53
                1150 Studium Ausbildung |         NA        0.00       88.53
        1152 Erwm.rnt Leistverm. 15-30S |         NA        0.03       88.56
                             1153 Umzug |         NA        0.03       88.59
                       1154 Altersrente |     12,217       11.05       99.64
                    1155 Unterhaltsgeld |         NA        0.00       99.64
                     1156 Kuraufenthalt |        146        0.13       99.77
                1157 Ausreise mit E 303 |         NA        0.00       99.77
           1158 Sperrzeit 3/6/12 Wochen |         NA        0.00       99.78
              1159 Ablauf der Massnahme |         NA        0.02       99.79
                  1160 Eigene Abmeldung |        174        0.16       99.95
         1161 Wegfall der Verfügbarkeit |         NA        0.01       99.96
                1162 3. Meldeversäumnis |         NA        0.01       99.97
        1163 Aufn. einer selbst. Tätigk |         NA        0.00       99.97
        1165 Ende Arbeitssuche im Ausla |         NA        0.00       99.97
        1166 Ablauf des Mitnahmezeitrau |         NA        0.00       99.97
        1167 Reha-MN mit Anspruch auf Ü |         NA        0.03      100.00
        1168 Reha-MN ohne Anspruch auf  |         NA        0.00      100.00
----------------------------------------+-----------------------------------
                                  Total |    110,598      100.00
r; t=0.45 14:44:03

.         di "lignite"
lignite
r; t=0.00 14:44:03

.                 tab thisspelllignite if dpotret==1 

 this spell |
 is lignite |
   industry |
 (mining OR |
  (services |
 in lignite |
    areas)) |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     67,291       77.97       77.97
          1 |     19,012       22.03      100.00
------------+-----------------------------------
      Total |     86,303      100.00
r; t=0.28 14:44:04

.                 tab thisspelllignite if dpotret==1 & agepotret>49

 this spell |
 is lignite |
   industry |
 (mining OR |
  (services |
 in lignite |
    areas)) |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     66,537       78.06       78.06
          1 |     18,702       21.94      100.00
------------+-----------------------------------
      Total |     85,239      100.00
r; t=0.42 14:44:04

.         di "statsimple=1"               
statsimple=1
r; t=0.00 14:44:04

.                 tab statsimple if dpotret==1

        0 - |
unemployed, |
 margemp or |
 ALMP / 1 - |
 employed / |
        2 - |
 vocational |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     53,960       62.52       62.52
          1 |     31,901       36.96       99.49
          2 |        442        0.51      100.00
------------+-----------------------------------
      Total |     86,303      100.00
r; t=0.26 14:44:04

.                 tab statsimple if dpotret==1 & agepotret>49

        0 - |
unemployed, |
 margemp or |
 ALMP / 1 - |
 employed / |
        2 - |
 vocational |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     53,418       62.67       62.67
          1 |     31,381       36.82       99.48
          2 |        440        0.52      100.00
------------+-----------------------------------
      Total |     85,239      100.00
r; t=0.40 14:44:05

. 
. 
. 
. /* ------------------------------------------------------------------------ */
.  *      (1) Define transition var: mutually exclusive var on post-lignite destinations
. /* ------------------------------------------------------------------------ */
.         
.         /* We use statsimple, which is 
>                         - 0 for unemployment, marginal employment or ALMP
>                         - 1 for normal employment
>                         - 2 for vocational employment
>         note we have collated sequential */
.                 
.         /* The transition variable should capture the following types of transitions:
>                         1) pretrans == 1        Lignite -> retirement (with no marginal employment)
>                         2) pretrans == 2        Lignite -> vocational
>                         3) pretrans == 3        Lignite -> other normalemp
>                         4) pretrans == 6        Lignite -> unemp/margemp/ALMP - retirement
>                         5) a) pretrans == 7     Lignite -> unemp/margemp/ALMP - other normalemp
>                            b) pretrans == 8 Lignite -> unemp - normalemp (in lignite)
>                         6) pretrans == 10       Lignite -> black hole
>                         7) pretrans == 11       NON-Lignite -> unemp/margemp/ALMP - normal emp not in lignite
>                         8) pretrans == 12       NON-Lignite -> unemp/margemp/ALMP - other */
. 
.                 cap drop pretrans
r; t=0.00 14:44:05

.                 gen pretrans = 0
r; t=0.05 14:44:05

. 
.                 * (Pretrans=1) Retirement (difficult: no direct indication in BeH - use last observed spell that not minijob (potret) - defined in 1prepare
.                 
.                 * 1 a) direct retirement out of unemployment (EXTREMELY FEW cases - grund 2022 & 2055 - case (6) more likely. for completeness here.
.                         bysort pid (begepi): replace pretrans = 1  if thisspelllignite == 1 & (grund == 509 | grund == 1154) & (agepotret>49 & agepotret!=.) & (dpotret==1)
(87 real changes made)
r; t=0.77 14:44:06

.                                                 
.                 * 1 b) retirement from employment: end of job (grund=130),  above a certain age (over49) & no later spell observed in data (potret - defined above) => retirement 
.                         bysort pid (begepi): replace pretrans = 1  if thisspelllignite == 1 & statsimple==1 & (grund==130) & (agepotret>49 & agepotret!=.) & (dpotret==1) 
(9864 real changes made)
r; t=0.14 14:44:06

.         
.                 * (Pretrans=2) Lignite - vocational
.                         bysort pid (begepi): replace pretrans = 2 if thisspelllignite == 1 & statsimple==1 & thisspelllignite[_n+1] == 0 & statsimple[_n+1]==2  
(159 real changes made)
r; t=0.11 14:44:06

.                         
.                 * (Pretrans=3) Lignite - normalemp
.                         bysort pid (begepi): replace pretrans = 3 if thisspelllignite == 1 & statsimple==1 & thisspelllignite[_n+1] == 0 & statsimple[_n+1]==1  
(39993 real changes made)
r; t=0.12 14:44:06

.                                                 
.                 * (Pretrans=6) Lignite - unemp/margemp/ALMP - retirement
.                         bysort pid (begepi): replace pretrans = 6 if thisspelllignite == 1 & statsimple==1 & statsimple[_n+1]==0 & (agepotret>49 & agepotret!=.) & (dpotret[_n+1]==1)  
(14713 real changes made)
r; t=0.14 14:44:06

.                 
.                 * (pretrans=7) Lignite - unemp/margemp/ALMP - normal employment (not in lignite)
.                         bysort pid (begepi): replace pretrans = 7 if thisspelllignite == 1 & statsimple==1 & statsimple[_n+1]==0 & thisspelllignite[_n+2] == 0 & (statsimple[_n+2]==1)
(22273 real changes made)
r; t=0.13 14:44:06

.                                         
.                 * (pretrans=8) Lignite - unemp/margemp/ALMP - normalemp (in lignite)
.                         bysort pid (begepi): replace pretrans = 8 if thisspelllignite == 1 & statsimple==1 & statsimple[_n+1]==0 & thisspelllignite[_n+2] == 1  & (statsimple[_n+2]==1)
(3873 real changes made)
r; t=0.13 14:44:06

.                                         
.                 * (pretrans=10) Lignite - black hole
.                         bysort pid (begepi): replace pretrans = 10 if thisspelllignite == 1 & thisspelllignite[_n+1] != 1 & status[_n+1] == 10
(51735 real changes made)
r; t=0.09 14:44:07

.                                 
.                 * (pretrans=11) NON-Lignite normal employment- unemp/margemp/ALMP - normal employment (not in lignite)
.                         bysort pid (begepi): replace pretrans = 11 if thisspelllignite == 0 & statsimple==1 & statsimple[_n+1]==0 & thisspelllignite[_n+2] == 0 & (statsimple[_n+2]==1) 
(171765 real changes made)
r; t=0.14 14:44:07

.                         
.                 * (pretrans=12) NON-Lignite normal employment- unemp/margemp/ALMP - employment in lignite
.                         bysort pid (begepi): replace pretrans = 12 if thisspelllignite == 0 & statsimple==1 & statsimple[_n+1]==0 & thisspelllignite[_n+2] == 1 & (statsimple[_n+2]==1)
(4493 real changes made)
r; t=0.12 14:44:07

.                                         
.                         * Remark: Here we only consider individuals that have another employment spell after their unemployment spell. 
.                                 *                 Individuals with series: ligemployment - nonligemployment - unemployment END will not be included in estimation
.                 
.                 label define pretransLAB        0 "Not pre (observed) trans'n out of lignite" ///
>                                                                         1 "pre retirement (without minijob)" ///
>                                                                         2 "pre trans'n 2 vocational training" ///
>                                                                         3 "pre trans'n 2 other normal employment" ///
>                                                                         6 "pre trans'n to unem/ALMP/marg,then retire" ///
>                                                                         7 "pre trans'n 2 unemp/ALMP/marg,then non-lig emp" ///
>                                                                         8 "pre trans'n 2 unemp/ALMP/marg,then lig emp" ///
>                                                                         10 "pre trans'n 2 black hole" ///
>                                                                         11 "pre transition out of NON-Lig into un/ALMP/marg ending in emp non lignite" ///
>                                                                         12 "pre transition out of NON-Lig into un/ALMP/marg ending in emp lignite"
r; t=0.00 14:44:07

.                                                          
.                 label values pretrans pretransLAB
r; t=0.00 14:44:07

.                 
.                 * Define the period after the transition
.                 sort pid begepi
r; t=0.23 14:44:07

.                 gen posttrans = 0
r; t=0.04 14:44:07

.                 bysort pid (begepi): replace posttrans = pretrans[_n-1] if pretrans[_n-1]!=.
(309318 real changes made)
r; t=0.07 14:44:07

.                 label define posttransLAB       0 "Not post trans'n out of lignite" ///
>                                                                         1 "in retirement (no minijob) post-lignite" ///
>                                                                         2 " post-lignite vocational training" ///
>                                                                         3 "post-lignite normal employment" ///
>                                                                         6 "post-lignite unemp/ALMP/marg ending in retirement" ///
>                                                                         7 "post-lignite unemp/ALMP/marg ending in another emp"  ///
>                                                                         8 "post-lignite unemp/ALMP/marg ending in lignite emp" ///
>                                                                         10 "post-lignite black hole" ///
>                                                                         11 "transition out of NON-Lig into un/ALMP/marg ending in emp non lignite" ///
>                                                                         12 "transition out of NON-Lig into un/ALMP/marg ending in emp lignite"
r; t=0.00 14:44:07

.                 label values posttrans posttransLAB
r; t=0.00 14:44:07

.                 
.                 * compare occurence of different transitions
.                 tab pretrans, m

                               pretrans |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
Not pre (observed) trans'n out of ligni |  1,137,595       78.13       78.13
       pre retirement (without minijob) |      9,443        0.65       78.78
      pre trans'n 2 vocational training |        159        0.01       78.79
  pre trans'n 2 other normal employment |     39,993        2.75       81.54
pre trans'n to unem/ALMP/marg,then reti |     14,709        1.01       82.55
pre trans'n 2 unemp/ALMP/marg,then non- |     22,273        1.53       84.08
pre trans'n 2 unemp/ALMP/marg,then lig  |      3,873        0.27       84.34
               pre trans'n 2 black hole |     51,735        3.55       87.89
pre transition out of NON-Lig into un/A |    171,765       11.80       99.69
pre transition out of NON-Lig into un/A |      4,493        0.31      100.00
----------------------------------------+-----------------------------------
                                  Total |  1,456,038      100.00
r; t=0.25 14:44:07

.                 tab posttrans, m

                              posttrans |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
        Not post trans'n out of lignite |  1,146,720       78.76       78.76
in retirement (no minijob) post-lignite |        318        0.02       78.78
       post-lignite vocational training |        159        0.01       78.79
         post-lignite normal employment |     39,993        2.75       81.54
post-lignite unemp/ALMP/marg ending in  |     14,709        1.01       82.55
post-lignite unemp/ALMP/marg ending in  |     22,273        1.53       84.08
post-lignite unemp/ALMP/marg ending in  |      3,873        0.27       84.34
                post-lignite black hole |     51,735        3.55       87.89
transition out of NON-Lig into un/ALMP/ |    171,765       11.80       99.69
transition out of NON-Lig into un/ALMP/ |      4,493        0.31      100.00
----------------------------------------+-----------------------------------
                                  Total |  1,456,038      100.00
r; t=0.29 14:44:08

.                 
.                 tab pretrans statsimple, m

                      |    0 - unemployed, margemp or ALMP / 1 -
                      |          employed / 2 - vocational
             pretrans |         0          1          2          . |     Total
----------------------+--------------------------------------------+----------
Not pre (observed) tr |   414,921    416,490     97,841    208,343 | 1,137,595 
pre retirement (witho |        NA      NA          0          0	   |        NA
pre trans'n 2 vocatio |         0        159          0          0 |       159 
pre trans'n 2 other n |         0     39,993          0          0 |    39,993 
pre trans'n to unem/A |         0     14,709          0          0 |    14,709 
pre trans'n 2 unemp/A |         0     22,273          0          0 |    22,273 
pre trans'n 2 unemp/A |         0      3,873          0          0 |     3,873 
pre trans'n 2 black h |     3,410     46,182      2,143          0 |    51,735 
pre transition out of |         0    171,765          0          0 |   171,765 
pre transition out of |         0      4,493          0          0 |     4,493 
----------------------+--------------------------------------------+----------
                Total |   418,408    729,303     99,984    208,343 | 1,456,038 
r; t=0.33 14:44:08

.                 tab posttrans statsimple, m

                      |    0 - unemployed, margemp or ALMP / 1 -
                      |          employed / 2 - vocational
            posttrans |         0          1          2          . |     Total
----------------------+--------------------------------------------+----------
Not post trans'n out  |   201,066    689,223     99,823    156,608 | 1,146,720 
in retirement (no min |       229         NA         NA          0 |       318 
 post-lignite vocatio |         0          0        159          0 |       159 
post-lignite normal e |         0     39,993          0          0 |    39,993 
post-lignite unemp/AL |    14,709          0          0          0 |    14,709 
post-lignite unemp/AL |    22,273          0          0          0 |    22,273 
post-lignite unemp/AL |     3,873          0          0          0 |     3,873 
post-lignite black ho |         0          0          0     51,735 |    51,735 
transition out of NON |   171,765          0          0          0 |   171,765 
transition out of NON |     4,493          0          0          0 |     4,493 
----------------------+--------------------------------------------+----------
                Total |   418,408    729,303     99,984    208,343 | 1,456,038 
r; t=0.36 14:44:08

.                 
.                 tab pretrans status, m

                      |                              status
             pretrans | unemploye  active_la  marginal_  normal_em  vocationa         10 |     Total
----------------------+------------------------------------------------------------------+----------
Not pre (observed) tr |   348,165     15,355     51,401    416,490     97,841    208,343 | 1,137,595 
pre retirement (witho |         0          0         NA         NA          0          0 |    	  NA 
pre trans'n 2 vocatio |         0          0          0        159          0          0 |       159 
pre trans'n 2 other n |         0          0          0     39,993          0          0 |    39,993 
pre trans'n to unem/A |         0          0          0     14,709          0          0 |    14,709 
pre trans'n 2 unemp/A |         0          0          0     22,273          0          0 |    22,273 
pre trans'n 2 unemp/A |         0          0          0      3,873          0          0 |     3,873 
pre trans'n 2 black h |         0          0      3,410     46,182      2,143          0 |    51,735 
pre transition out of |         0          0          0    171,765          0          0 |   171,765 
pre transition out of |         0          0          0      4,493          0          0 |     4,493 
----------------------+------------------------------------------------------------------+----------
                Total |   348,165     15,355     54,888    729,303     99,984    208,343 | 1,456,038 
r; t=0.35 14:44:09

.                 tab posttrans status, m

                      |                              status
            posttrans | unemploye  active_la  marginal_  normal_em  vocationa         10 |     Total
----------------------+------------------------------------------------------------------+----------
Not post trans'n out  |   147,239      6,897     46,930    689,223     99,823    156,608 | 1,146,720 
in retirement (no min |        NA          0        161         NA         NA          0 |       318 
 post-lignite vocatio |         0          0          0          0        159          0 |       159 
post-lignite normal e |         0          0          0     39,993          0          0 |    39,993 
post-lignite unemp/AL |    14,605         39         65          0          0          0 |    14,709 
post-lignite unemp/AL |    20,744      1,376        153          0          0          0 |    22,273 
post-lignite unemp/AL |     3,100        343        430          0          0          0 |     3,873 
post-lignite black ho |         0          0          0          0          0     51,735 |    51,735 
transition out of NON |   158,217      6,525      7,023          0          0          0 |   171,765 
transition out of NON |     4,192        175        126          0          0          0 |     4,493 
----------------------+------------------------------------------------------------------+----------
                Total |   348,165     15,355     54,888    729,303     99,984    208,343 | 1,456,038 
r; t=0.37 14:44:09

.                 
. /* ------------------------------------------------------------------------ */
.  *  (2) Post-lignite destinations
. /* ------------------------------------------------------------------------ */  
. *
. *   (2.0) Overview of post-lignite destinations
. *
.                 ************************************************************************
.                 * Transition destinations after lignite
.                 ************************************************************************
.                 * from part (1b) of 4transitions
.                 
.                 * post-lignite destinations by decade *
.                 disp("Transitions out of lignite in the 1970s and 1980s")
Transitions out of lignite in the 1970s and 1980s
r; t=0.00 14:44:09

.                 tab pretrans if pretrans>0 & thisspelllignite==1 & pretrans!=. & decades==1

                               pretrans |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
       pre retirement (without minijob) |      5,683       18.44       18.44
      pre trans'n 2 vocational training |         NA        0.19       18.63
  pre trans'n 2 other normal employment |      4,644       15.06       33.69
pre trans'n to unem/ALMP/marg,then reti |      1,370        4.44       38.14
pre trans'n 2 unemp/ALMP/marg,then non- |        868        2.82       40.95
pre trans'n 2 unemp/ALMP/marg,then lig  |        349        1.13       42.09
               pre trans'n 2 black hole |     17,853       57.91      100.00
----------------------------------------+-----------------------------------
                                  Total |     30,827      100.00
r; t=0.47 14:44:10

.                 disp("Transitions out of lignite in the 1990s")         
Transitions out of lignite in the 1990s
r; t=0.00 14:44:10

.                 tab pretrans if pretrans>0 & thisspelllignite==1 & pretrans!=. & decades==2

                               pretrans |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
       pre retirement (without minijob) |      1,284        1.55        1.55
      pre trans'n 2 vocational training |         NA        0.06        1.62
  pre trans'n 2 other normal employment |     27,940       33.82       35.44
pre trans'n to unem/ALMP/marg,then reti |     10,846       13.13       48.57
pre trans'n 2 unemp/ALMP/marg,then non- |     18,542       22.45       71.02
pre trans'n 2 unemp/ALMP/marg,then lig  |      2,699        3.27       74.29
               pre trans'n 2 black hole |     21,241       25.71      100.00
----------------------------------------+-----------------------------------
                                  Total |     82,605      100.00
r; t=0.47 14:44:10

.                 disp("Transitions out of lignite in the 2000s")
Transitions out of lignite in the 2000s
r; t=0.00 14:44:10

.                 tab pretrans if pretrans>0 & thisspelllignite==1 & pretrans!=. & decades==3

                               pretrans |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
       pre retirement (without minijob) |        596        3.24        3.24
      pre trans'n 2 vocational training |         NA        0.11        3.35
  pre trans'n 2 other normal employment |      5,479       29.78       33.13
pre trans'n to unem/ALMP/marg,then reti |      2,353       12.79       45.91
pre trans'n 2 unemp/ALMP/marg,then non- |      2,494       13.55       59.47
pre trans'n 2 unemp/ALMP/marg,then lig  |        679        3.69       63.16
               pre trans'n 2 black hole |      6,779       36.84      100.00
----------------------------------------+-----------------------------------
                                  Total |     18,400      100.00
r; t=0.45 14:44:11

.                 disp("Transitions out of lignite in the 2010s")
Transitions out of lignite in the 2010s
r; t=0.00 14:44:11

.                 tab pretrans if pretrans>0 & thisspelllignite==1 & pretrans!=. & decades==4

                               pretrans |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
       pre retirement (without minijob) |      1,880       18.16       18.16
      pre trans'n 2 vocational training |         NA        0.25       18.41
  pre trans'n 2 other normal employment |      1,930       18.64       37.05
pre trans'n to unem/ALMP/marg,then reti |        140        1.35       38.40
pre trans'n 2 unemp/ALMP/marg,then non- |        369        3.56       41.97
pre trans'n 2 unemp/ALMP/marg,then lig  |        146        1.41       43.38
               pre trans'n 2 black hole |      5,862       56.62      100.00
----------------------------------------+-----------------------------------
                                  Total |     10,353      100.00
r; t=0.44 14:44:11

.         
.                 * Check: this should only give zeros (no transition) and ones (for retirement we have no post-trans obs)
.                 tab pretrans if posttrans[_n+1]!=pretrans 

                               pretrans |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
Not pre (observed) trans'n out of ligni |         NA        0.01        0.01
       pre retirement (without minijob) |      9,125       99.99      100.00
----------------------------------------+-----------------------------------
                                  Total |      9,126      100.00
r; t=0.33 14:44:11

. 
.                 * Pretrans - ageendcat (age at the end of spell)
.                 tab pretrans ageendcat if pretrans>0 & thisspelllignite == 1, column row 

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                      |   age at end of spell by broad
                      |             category
             pretrans | 18-30 yea  30-50 yea  over 50 y |     Total
----------------------+---------------------------------+----------
pre retirement (witho |         0         NA      9,390 |     9,443 
                      |      0.00       0.56      99.44 |    100.00 
                      |      0.00       0.09      18.01 |      6.64 
----------------------+---------------------------------+----------
pre trans'n 2 vocatio |       106         NA         NA |       159 
                      |     66.67      22.01      11.32 |    100.00 
                      |      0.36       0.06       0.03 |      0.11 
----------------------+---------------------------------+----------
pre trans'n 2 other n |     8,349     23,838      7,806 |    39,993 
                      |     20.88      59.61      19.52 |    100.00 
                      |     28.33      39.36      14.97 |     28.13 
----------------------+---------------------------------+----------
pre trans'n to unem/A |         0        405     14,304 |    14,709 
                      |      0.00       2.75      97.25 |    100.00 
                      |      0.00       0.67      27.43 |     10.34 
----------------------+---------------------------------+----------
pre trans'n 2 unemp/A |     6,943     12,746      2,584 |    22,273 
                      |     31.17      57.23      11.60 |    100.00 
                      |     23.56      21.05       4.95 |     15.66 
----------------------+---------------------------------+----------
pre trans'n 2 unemp/A |       783      2,126        964 |     3,873 
                      |     20.22      54.89      24.89 |    100.00 
                      |      2.66       3.51       1.85 |      2.72 
----------------------+---------------------------------+----------
pre trans'n 2 black h |    13,289     21,362     17,084 |    51,735 
                      |     25.69      41.29      33.02 |    100.00 
                      |     45.09      35.27      32.76 |     36.39 
----------------------+---------------------------------+----------
                Total |    29,470     60,565     52,150 |   142,185 
                      |     20.73      42.60      36.68 |    100.00 
                      |    100.00     100.00     100.00 |    100.00 
r; t=0.36 14:44:12

.                 *
. 
.                 * post-lignite destinations by decade & age *
.                 *
.                 disp("Transitions out of lignite in the 1970s and 1980s")
Transitions out of lignite in the 1970s and 1980s
r; t=0.00 14:44:12

.                 tab pretrans ageendcat if pretrans>0 & pretrans!=. & thisspelllignite == 1 & decades==1, column row

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                      |   age at end of spell by broad
                      |             category
             pretrans | 18-30 yea  30-50 yea  over 50 y |     Total
----------------------+---------------------------------+----------
pre retirement (witho |         0         NA      5,650 |     5,683 
                      |      0.00       0.58      99.42 |    100.00 
                      |      0.00       0.38      40.44 |     18.44 
----------------------+---------------------------------+----------
pre trans'n 2 vocatio |        NA         NA         NA |        NA 
                      |     53.33      23.33      23.33 |     100.00 
                      |      0.39       0.16       0.10 |      0.19 
----------------------+---------------------------------+----------
pre trans'n 2 other n |     1,490      2,260        894 |     4,644 
                      |     32.08      48.66      19.25 |    100.00 
                      |     18.07      26.25       6.40 |     15.06 
----------------------+---------------------------------+----------
pre trans'n to unem/A |         0         NA      1,353 |     1,370 
                      |      0.00       1.24      98.76 |    100.00 
                      |      0.00       0.20       9.68 |      4.44 
----------------------+---------------------------------+----------
pre trans'n 2 unemp/A |       359        389        120 |       868 
                      |     41.36      44.82      13.82 |    100.00 
                      |      4.35       4.52       0.86 |      2.82 
----------------------+---------------------------------+----------
pre trans'n 2 unemp/A |       129        157         NA |       349 
                      |     36.96      44.99      18.05 |    100.00 
                      |      1.56       1.82       0.45 |      1.13 
----------------------+---------------------------------+----------
pre trans'n 2 black h |     6,236      5,738      5,879 |    17,853 
                      |     34.93      32.14      32.93 |    100.00 
                      |     75.62      66.66      42.07 |     57.91 
----------------------+---------------------------------+----------
                Total |     8,246      8,608     13,973 |    30,827 
                      |     26.75      27.92      45.33 |    100.00 
                      |    100.00     100.00     100.00 |    100.00 
r; t=0.55 14:44:12

.                 disp("Transitions out of lignite in the 1990s")         
Transitions out of lignite in the 1990s
r; t=0.00 14:44:12

.                 tab pretrans  ageendcat if pretrans>0 & pretrans!=. & thisspelllignite == 1 & decades==2, column row

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                      |   age at end of spell by broad
                      |             category
             pretrans | 18-30 yea  30-50 yea  over 50 y |     Total
----------------------+---------------------------------+----------
pre retirement (witho |         0         NA      1,268 |     1,284 
                      |      0.00       1.25      98.75 |    100.00 
                      |      0.00       0.04       5.18 |      1.55 
----------------------+---------------------------------+----------
pre trans'n 2 vocatio |        NA         NA         NA |        NA 
                      |     64.15      33.96       1.89 |    100.00 
                      |      0.20       0.04       0.00 |      0.06 
----------------------+---------------------------------+----------
pre trans'n 2 other n |     5,915     17,535      4,490 |    27,940 
                      |     21.17      62.76      16.07 |    100.00 
                      |     34.79      42.65      18.33 |     33.82 
----------------------+---------------------------------+----------
pre trans'n to unem/A |         0        338     10,508 |    10,846 
                      |      0.00       3.12      96.88 |    100.00 
                      |      0.00       0.82      42.90 |     13.13 
----------------------+---------------------------------+----------
pre trans'n 2 unemp/A |     5,700     10,996      1,846 |    18,542 
                      |     30.74      59.30       9.96 |    100.00 
                      |     33.52      26.75       7.54 |     22.45 
----------------------+---------------------------------+----------
pre trans'n 2 unemp/A |       502      1,611        586 |     2,699 
                      |     18.60      59.69      21.71 |    100.00 
                      |      2.95       3.92       2.39 |      3.27 
----------------------+---------------------------------+----------
pre trans'n 2 black h |     4,853     10,595      5,793 |    21,241 
                      |     22.85      49.88      27.27 |    100.00 
                      |     28.54      25.77      23.65 |     25.71 
----------------------+---------------------------------+----------
                Total |    17,004     41,109     24,492 |    82,605 
                      |     20.58      49.77      29.65 |    100.00 
                      |    100.00     100.00     100.00 |    100.00 
r; t=0.55 14:44:13

.                 disp("Transitions out of lignite in the 2000s")
Transitions out of lignite in the 2000s
r; t=0.00 14:44:13

.                 tab pretrans  ageendcat if pretrans>0 & pretrans!=. & thisspelllignite == 1 & decades==3, column row

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                      |   age at end of spell by broad
                      |             category
             pretrans | 18-30 yea  30-50 yea  over 50 y |     Total
----------------------+---------------------------------+----------
pre retirement (witho |         0         NA        593 |       596 
                      |      0.00       0.50      99.50 |    100.00 
                      |      0.00       0.03       8.16 |      3.24 
----------------------+---------------------------------+----------
pre trans'n 2 vocatio |        NA         NA          0 |        20 
                      |     95.00       5.00       0.00 |    100.00 
                      |      0.76       0.01       0.00 |      0.11 
----------------------+---------------------------------+----------
pre trans'n 2 other n |       524      3,402      1,553 |     5,479 
                      |      9.56      62.09      28.34 |    100.00 
                      |     21.08      39.34      21.37 |     29.78 
----------------------+---------------------------------+----------
pre trans'n to unem/A |         0         NA      2,307 |     2,353 
                      |      0.00       1.95      98.05 |    100.00 
                      |      0.00       0.53      31.75 |     12.79 
----------------------+---------------------------------+----------
pre trans'n 2 unemp/A |       688      1,268        538 |     2,494 
                      |     27.59      50.84      21.57 |    100.00 
                      |     27.67      14.66       7.40 |     13.55 
----------------------+---------------------------------+----------
pre trans'n 2 unemp/A |       133        310        236 |       679 
                      |     19.59      45.66      34.76 |    100.00 
                      |      5.35       3.59       3.25 |      3.69 
----------------------+---------------------------------+----------
pre trans'n 2 black h |     1,122      3,617      2,040 |     6,779 
                      |     16.55      53.36      30.09 |    100.00 
                      |     45.13      41.83      28.07 |     36.84 
----------------------+---------------------------------+----------
                Total |     2,486      8,647      7,267 |    18,400 
                      |     13.51      46.99      39.49 |    100.00 
                      |    100.00     100.00     100.00 |    100.00 
r; t=0.51 14:44:13

.                 disp("Transitions out of lignite in the 2010s")
Transitions out of lignite in the 2010s
r; t=0.00 14:44:13

.                 tab pretrans  ageendcat if pretrans>0 & pretrans!=. & thisspelllignite == 1 & decades==4, column row                    

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                      |   age at end of spell by broad
                      |             category
             pretrans | 18-30 yea  30-50 yea  over 50 y |     Total
----------------------+---------------------------------+----------
pre retirement (witho |         0         NA      1,879 |     1,880 
                      |      0.00       0.05      99.95 |    100.00 
                      |      0.00       0.05      29.28 |     18.16 
----------------------+---------------------------------+----------
pre trans'n 2 vocatio |        NA         NA         NA |        NA 
                      |     80.77       7.69      11.54 |    100.00 
                      |      1.21       0.09       0.05 |      0.25 
----------------------+---------------------------------+----------
pre trans'n 2 other n |       420        641        869 |     1,930 
                      |     21.76      33.21      45.03 |    100.00 
                      |     24.22      29.12      13.54 |     18.64 
----------------------+---------------------------------+----------
pre trans'n to unem/A |         0         NA        136 |       140 
                      |      0.00       2.86      97.14 |    100.00 
                      |      0.00       0.18       2.12 |      1.35 
----------------------+---------------------------------+----------
pre trans'n 2 unemp/A |       196         NA         NA |       369 
                      |     53.12      25.20      21.68 |    100.00 
                      |     11.30       4.23       1.25 |      3.56 
----------------------+---------------------------------+----------
pre trans'n 2 unemp/A |        NA         NA         NA |       146 
                      |     13.01      32.88      54.11 |    100.00 
                      |      1.10       2.18       1.23 |      1.41 
----------------------+---------------------------------+----------
pre trans'n 2 black h |     1,078      1,412      3,372 |     5,862 
                      |     18.39      24.09      57.52 |    100.00 
                      |     62.17      64.15      52.54 |     56.62 
----------------------+---------------------------------+----------
                Total |     1,734      2,201      6,418 |    10,353 
                      |     16.75      21.26      61.99 |    100.00 
                      |    100.00     100.00     100.00 |    100.00 
r; t=0.48 14:44:14

.         
.                 *  post-lignite destinations - by decade & gender *
.                 *
.                 tab pretrans frau if pretrans>0, column row

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                      |         frau
             pretrans |      mann       frau |     Total
----------------------+----------------------+----------
pre retirement (witho |     8,642        801 |     9,443 
                      |     91.52       8.48 |    100.00 
                      |      3.39       1.26 |      2.97 
----------------------+----------------------+----------
pre trans'n 2 vocatio |       133         NA |       159 
                      |     83.65      16.35 |    100.00 
                      |      0.05       0.04 |      0.05 
----------------------+----------------------+----------
pre trans'n 2 other n |    33,265      6,728 |    39,993 
                      |     83.18      16.82 |    100.00 
                      |     13.05      10.59 |     12.56 
----------------------+----------------------+----------
pre trans'n to unem/A |    11,601      3,108 |    14,709 
                      |     78.87      21.13 |    100.00 
                      |      4.55       4.89 |      4.62 
----------------------+----------------------+----------
pre trans'n 2 unemp/A |    15,152      7,121 |    22,273 
                      |     68.03      31.97 |    100.00 
                      |      5.94      11.21 |      6.99 
----------------------+----------------------+----------
pre trans'n 2 unemp/A |     2,970        903 |     3,873 
                      |     76.68      23.32 |    100.00 
                      |      1.16       1.42 |      1.22 
----------------------+----------------------+----------
pre trans'n 2 black h |    43,683      8,052 |    51,735 
                      |     84.44      15.56 |    100.00 
                      |     17.13      12.68 |     16.25 
----------------------+----------------------+----------
pre transition out of |   135,564     36,201 |   171,765 
                      |     78.92      21.08 |    100.00 
                      |     53.18      57.00 |     53.94 
----------------------+----------------------+----------
pre transition out of |     3,926        567 |     4,493 
                      |     87.38      12.62 |    100.00 
                      |      1.54       0.89 |      1.41 
----------------------+----------------------+----------
                Total |   254,936     63,507 |   318,443 
                      |     80.06      19.94 |    100.00 
                      |    100.00     100.00 |    100.00 
r; t=0.33 14:44:14

.                 * post-lignite destinations by decade & gender*
.                 disp("Transitions out of lignite in the 1970s and 1980s")
Transitions out of lignite in the 1970s and 1980s
r; t=0.00 14:44:14

.                 tab pretrans frau if pretrans>0 & pretrans!=. & thisspelllignite == 1 & decades==1, column row

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                      |         frau
             pretrans |      mann       frau |     Total
----------------------+----------------------+----------
pre retirement (witho |     5,417        266 |     5,683 
                      |     95.32       4.68 |    100.00 
                      |     18.91      12.16 |     18.44 
----------------------+----------------------+----------
pre trans'n 2 vocatio |        NA         NA |        NA 
                      |     96.67       3.33 |    100.00 
                      |      0.20       0.09 |      0.19 
----------------------+----------------------+----------
pre trans'n 2 other n |     4,464        180 |     4,644 
                      |     96.12       3.88 |    100.00 
                      |     15.59       8.23 |     15.06 
----------------------+----------------------+----------
pre trans'n to unem/A |     1,331         NA |     1,370 
                      |     97.15       2.85 |    100.00 
                      |      4.65       1.78 |      4.44 
----------------------+----------------------+----------
pre trans'n 2 unemp/A |       780         NA |       868 
                      |     89.86      10.14 |    100.00 
                      |      2.72       4.02 |      2.82 
----------------------+----------------------+----------
pre trans'n 2 unemp/A |       156        193 |       349 
                      |     44.70      55.30 |    100.00 
                      |      0.54       8.82 |      1.13 
----------------------+----------------------+----------
pre trans'n 2 black h |    16,433      1,420 |    17,853 
                      |     92.05       7.95 |    100.00 
                      |     57.38      64.90 |     57.91 
----------------------+----------------------+----------
                Total |    28,639      2,188 |    30,827 
                      |     92.90       7.10 |    100.00 
                      |    100.00     100.00 |    100.00 
r; t=0.51 14:44:15

.                 disp("Transitions out of lignite in the 1990s")         
Transitions out of lignite in the 1990s
r; t=0.00 14:44:15

.                 tab pretrans  frau if pretrans>0 & pretrans!=. & thisspelllignite == 1 & decades==2, column row

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                      |         frau
             pretrans |      mann       frau |     Total
----------------------+----------------------+----------
pre retirement (witho |     1,192         NA |     1,284 
                      |     92.83       7.17 |    100.00 
                      |      1.90       0.46 |      1.55 
----------------------+----------------------+----------
pre trans'n 2 vocatio |        NA         NA |        NA 
                      |     67.92      32.08 |    100.00 
                      |      0.06       0.09 |      0.06 
----------------------+----------------------+----------
pre trans'n 2 other n |    22,712      5,228 |    27,940 
                      |     81.29      18.71 |    100.00 
                      |     36.26      26.17 |     33.82 
----------------------+----------------------+----------
pre trans'n to unem/A |     8,150      2,696 |    10,846 
                      |     75.14      24.86 |    100.00 
                      |     13.01      13.50 |     13.13 
----------------------+----------------------+----------
pre trans'n 2 unemp/A |    12,073      6,469 |    18,542 
                      |     65.11      34.89 |    100.00 
                      |     19.28      32.39 |     22.45 
----------------------+----------------------+----------
pre trans'n 2 unemp/A |     2,112        587 |     2,699 
                      |     78.25      21.75 |    100.00 
                      |      3.37       2.94 |      3.27 
----------------------+----------------------+----------
pre trans'n 2 black h |    16,356      4,885 |    21,241 
                      |     77.00      23.00 |    100.00 
                      |     26.11      24.46 |     25.71 
----------------------+----------------------+----------
                Total |    62,631     19,974 |    82,605 
                      |     75.82      24.18 |    100.00 
                      |    100.00     100.00 |    100.00 
r; t=0.53 14:44:15

.                 disp("Transitions out of lignite in the 2000s")
Transitions out of lignite in the 2000s
r; t=0.00 14:44:15

.                 tab pretrans  frau if pretrans>0 & pretrans!=. & thisspelllignite == 1 & decades==3, column row

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                      |         frau
             pretrans |      mann       frau |     Total
----------------------+----------------------+----------
pre retirement (witho |       466        130 |       596 
                      |     78.19      21.81 |    100.00 
                      |      3.04       4.27 |      3.24 
----------------------+----------------------+----------
pre trans'n 2 vocatio |        NA         NA |        NA 
                      |     85.00      15.00 |    100.00 
                      |      0.11       0.10 |      0.11 
----------------------+----------------------+----------
pre trans'n 2 other n |     4,504        975 |     5,479 
                      |     82.20      17.80 |    100.00 
                      |     29.34      32.00 |     29.78 
----------------------+----------------------+----------
pre trans'n to unem/A |     1,989        364 |     2,353 
                      |     84.53      15.47 |    100.00 
                      |     12.96      11.95 |     12.79 
----------------------+----------------------+----------
pre trans'n 2 unemp/A |     1,986        508 |     2,494 
                      |     79.63      20.37 |    100.00 
                      |     12.94      16.67 |     13.55 
----------------------+----------------------+----------
pre trans'n 2 unemp/A |       585         NA |       679 
                      |     86.16      13.84 |    100.00 
                      |      3.81       3.09 |      3.69 
----------------------+----------------------+----------
pre trans'n 2 black h |     5,806        973 |     6,779 
                      |     85.65      14.35 |    100.00 
                      |     37.82      31.93 |     36.84 
----------------------+----------------------+----------
                Total |    15,353      3,047 |    18,400 
                      |     83.44      16.56 |    100.00 
                      |    100.00     100.00 |    100.00 
r; t=0.51 14:44:16

.                 disp("Transitions out of lignite in the 2010s")
Transitions out of lignite in the 2010s
r; t=0.00 14:44:16

.                 tab pretrans  frau if pretrans>0 & pretrans!=. & thisspelllignite == 1 & decades==4     , column row            

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                      |         frau
             pretrans |      mann       frau |     Total
----------------------+----------------------+----------
pre retirement (witho |     1,567        313 |     1,880 
                      |     83.35      16.65 |    100.00 
                      |     17.76      20.46 |     18.16 
----------------------+----------------------+----------
pre trans'n 2 vocatio |        NA         NA |        NA 
                      |     84.62      15.38 |    100.00 
                      |      0.25       0.26 |      0.25 
----------------------+----------------------+----------
pre trans'n 2 other n |     1,585        345 |     1,930 
                      |     82.12      17.88 |    100.00 
                      |     17.96      22.55 |     18.64 
----------------------+----------------------+----------
pre trans'n to unem/A |       131         NA |       140 
                      |     93.57       6.43 |    100.00 
                      |      1.48       0.59 |      1.35 
----------------------+----------------------+----------
pre trans'n 2 unemp/A |       313         NA |       369 
                      |     84.82      15.18 |    100.00 
                      |      3.55       3.66 |      3.56 
----------------------+----------------------+----------
pre trans'n 2 unemp/A |       117         NA |       146 
                      |     80.14      19.86 |    100.00 
                      |      1.33       1.90 |      1.41 
----------------------+----------------------+----------
pre trans'n 2 black h |     5,088        774 |     5,862 
                      |     86.80      13.20 |    100.00 
                      |     57.67      50.59 |     56.62 
----------------------+----------------------+----------
                Total |     8,823      1,530 |    10,353 
                      |     85.22      14.78 |    100.00 
                      |    100.00     100.00 |    100.00 
r; t=0.51 14:44:16

.                 
.                 *  post-lignite destinations - by decade & education *
.                 *
.                 tab pretrans bild if pretrans>0, column row

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                      |   Education, imputed based on
                      |    Fitzenberger, Osikominu &
                      |          Voelter (2008)
             pretrans | 1 no voca  2 vocatio  3 univers |     Total
----------------------+---------------------------------+----------
pre retirement (witho |     2,977      4,786      1,197 |     8,960 
                      |     33.23      53.42      13.36 |    100.00 
                      |      9.87       1.82       5.40 |      2.85 
----------------------+---------------------------------+----------
pre trans'n 2 vocatio |        NA         NA         NA |       155 
                      |     43.87      49.68       6.45 |    100.00 
                      |      0.23       0.03       0.05 |      0.05 
----------------------+---------------------------------+----------
pre trans'n 2 other n |     4,541     30,242      4,667 |    39,450 
                      |     11.51      76.66      11.83 |    100.00 
                      |     15.05      11.53      21.04 |     12.54 
----------------------+---------------------------------+----------
pre trans'n to unem/A |     2,164     10,120      1,625 |    13,909 
                      |     15.56      72.76      11.68 |    100.00 
                      |      7.17       3.86       7.32 |      4.42 
----------------------+---------------------------------+----------
pre trans'n 2 unemp/A |     2,243     18,836      1,125 |    22,204 
                      |     10.10      84.83       5.07 |    100.00 
                      |      7.43       7.18       5.07 |      7.06 
----------------------+---------------------------------+----------
pre trans'n 2 unemp/A |       358      3,227        251 |     3,836 
                      |      9.33      84.12       6.54 |    100.00 
                      |      1.19       1.23       1.13 |      1.22 
----------------------+---------------------------------+----------
pre trans'n 2 black h |    13,194     31,723      5,033 |    49,950 
                      |     26.41      63.51      10.08 |    100.00 
                      |     43.73      12.09      22.69 |     15.87 
----------------------+---------------------------------+----------
pre transition out of |     4,347    159,345      8,000 |   171,692 
                      |      2.53      92.81       4.66 |    100.00 
                      |     14.41      60.75      36.06 |     54.57 
----------------------+---------------------------------+----------
pre transition out of |       281      3,933        278 |     4,492 
                      |      6.26      87.56       6.19 |    100.00 
                      |      0.93       1.50       1.25 |      1.43 
----------------------+---------------------------------+----------
                Total |    30,173    262,289     22,186 |   314,648 
                      |      9.59      83.36       7.05 |    100.00 
                      |    100.00     100.00     100.00 |    100.00 
r; t=0.33 14:44:17

.                 * post-lignite destinations by decade & education*
.                 disp("Transitions out of lignite in the 1970s and 1980s")
Transitions out of lignite in the 1970s and 1980s
r; t=0.00 14:44:17

.                 tab pretrans bild if pretrans>0 & pretrans!=. & thisspelllignite == 1 & decades==1, column row

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                      |   Education, imputed based on
                      |    Fitzenberger, Osikominu &
                      |          Voelter (2008)
             pretrans | 1 no voca  2 vocatio  3 univers |     Total
----------------------+---------------------------------+----------
pre retirement (witho |     2,524      2,814        344 |     5,682 
                      |     44.42      49.52       6.05 |    100.00 
                      |     20.24      17.63      17.06 |     18.66 
----------------------+---------------------------------+----------
pre trans'n 2 vocatio |        NA         NA         NA |        NA 
                      |     65.00      30.00       5.00 |    100.00 
                      |      0.31       0.11       0.15 |      0.20 
----------------------+---------------------------------+----------
pre trans'n 2 other n |     1,892      2,336        387 |     4,615 
                      |     41.00      50.62       8.39 |    100.00 
                      |     15.17      14.63      19.19 |     15.15 
----------------------+---------------------------------+----------
pre trans'n to unem/A |       595        727         NA |     1,359 
                      |     43.78      53.50       2.72 |    100.00 
                      |      4.77       4.55       1.83 |      4.46 
----------------------+---------------------------------+----------
pre trans'n 2 unemp/A |       320        516         NA |       866 
                      |     36.95      59.58       3.46 |    100.00 
                      |      2.57       3.23       1.49 |      2.84 
----------------------+---------------------------------+----------
pre trans'n 2 unemp/A |       119        224         NA |       349 
                      |     34.10      64.18       1.72 |    100.00 
                      |      0.95       1.40       0.30 |      1.15 
----------------------+---------------------------------+----------
pre trans'n 2 black h |     6,984      9,328      1,210 |    17,522 
                      |     39.86      53.24       6.91 |    100.00 
                      |     55.99      58.44      59.99 |     57.54 
----------------------+---------------------------------+----------
                Total |    12,473     15,963      2,017 |    30,453 
                      |     40.96      52.42       6.62 |    100.00 
                      |    100.00     100.00     100.00 |    100.00 
r; t=0.50 14:44:17

.                 disp("Transitions out of lignite in the 1990s")         
Transitions out of lignite in the 1990s
r; t=0.00 14:44:17

.                 tab pretrans  bild if pretrans>0 & pretrans!=. & thisspelllignite == 1 & decades==2, column row

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                      |   Education, imputed based on
                      |    Fitzenberger, Osikominu &
                      |          Voelter (2008)
             pretrans | 1 no voca  2 vocatio  3 univers |     Total
----------------------+---------------------------------+----------
pre retirement (witho |       341        615        198 |     1,154 
                      |     29.55      53.29      17.16 |    100.00 
                      |      3.92       0.95       2.87 |      1.44 
----------------------+---------------------------------+----------
pre trans'n 2 vocatio |        NA         NA         NA |        NA 
                      |     30.61      57.14      12.24 |    100.00 
                      |      0.17       0.04       0.09 |      0.06 
----------------------+---------------------------------+----------
pre trans'n 2 other n |     1,838     23,544      2,238 |    27,620 
                      |      6.65      85.24       8.10 |    100.00 
                      |     21.13      36.33      32.49 |     34.36 
----------------------+---------------------------------+----------
pre trans'n to unem/A |     1,348      7,649      1,066 |    10,063 
                      |     13.40      76.01      10.59 |    100.00 
                      |     15.49      11.80      15.47 |     12.52 
----------------------+---------------------------------+----------
pre trans'n 2 unemp/A |     1,466     16,160        866 |    18,492 
                      |      7.93      87.39       4.68 |    100.00 
                      |     16.85      24.94      12.57 |     23.00 
----------------------+---------------------------------+----------
pre trans'n 2 unemp/A |       167      2,372        138 |     2,677 
                      |      6.24      88.61       5.16 |    100.00 
                      |      1.92       3.66       2.00 |      3.33 
----------------------+---------------------------------+----------
pre trans'n 2 black h |     3,525     14,433      2,377 |    20,335 
                      |     17.33      70.98      11.69 |    100.00 
                      |     40.52      22.27      34.50 |     25.30 
----------------------+---------------------------------+----------
                Total |     8,700     64,801      6,889 |    80,390 
                      |     10.82      80.61       8.57 |    100.00 
                      |    100.00     100.00     100.00 |    100.00 
r; t=0.55 14:44:18

.                 disp("Transitions out of lignite in the 2000s")
Transitions out of lignite in the 2000s
r; t=0.00 14:44:18

.                 tab pretrans  bild if pretrans>0 & pretrans!=. & thisspelllignite == 1 & decades==3, column row

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                      |   Education, imputed based on
                      |    Fitzenberger, Osikominu &
                      |          Voelter (2008)
             pretrans | 1 no voca  2 vocatio  3 univers |     Total
----------------------+---------------------------------+----------
pre retirement (witho |        NA        282        193 |       494 
                      |      3.85      57.09      39.07 |    100.00 
                      |      0.69       2.39       6.02 |      2.78 
----------------------+---------------------------------+----------
pre trans'n 2 vocatio |        NA         NA         NA |        NA 
                      |     30.00      70.00       0.00 |    100.00 
                      |      0.22       0.12       0.00 |      0.11 
----------------------+---------------------------------+----------
pre trans'n 2 other n |       601      3,178      1,511 |     5,290 
                      |     11.36      60.08      28.56 |    100.00 
                      |     21.75      26.96      47.16 |     29.79 
----------------------+---------------------------------+----------
pre trans'n to unem/A |       201      1,652        496 |     2,349 
                      |      8.56      70.33      21.12 |    100.00 
                      |      7.27      14.01      15.48 |     13.23 
----------------------+---------------------------------+----------
pre trans'n 2 unemp/A |       405      1,893        182 |     2,480 
                      |     16.33      76.33       7.34 |    100.00 
                      |     14.66      16.06       5.68 |     13.97 
----------------------+---------------------------------+----------
pre trans'n 2 unemp/A |        NA        531         NA |       668 
                      |      7.78      79.49      12.72 |    100.00 
                      |      1.88       4.50       2.65 |      3.76 
----------------------+---------------------------------+----------
pre trans'n 2 black h |     1,479      4,238        737 |     6,454 
                      |     22.92      65.66      11.42 |    100.00 
                      |     53.53      35.95      23.00 |     36.35 
----------------------+---------------------------------+----------
                Total |     2,763     11,788      3,204 |    17,755 
                      |     15.56      66.39      18.05 |    100.00 
                      |    100.00     100.00     100.00 |    100.00 
r; t=0.53 14:44:18

.                 disp("Transitions out of lignite in the 2010s")
Transitions out of lignite in the 2010s
r; t=0.00 14:44:18

.                 tab pretrans  bild if pretrans>0 & pretrans!=. & thisspelllignite == 1 & decades==4, column row                 

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                      |   Education, imputed based on
                      |    Fitzenberger, Osikominu &
                      |          Voelter (2008)
             pretrans | 1 no voca  2 vocatio  3 univers |     Total
----------------------+---------------------------------+----------
pre retirement (witho |        NA      1,075        462 |     1,630 
                      |      5.71      65.95      28.34 |    100.00 
                      |      5.78      16.64      25.70 |     16.52 
----------------------+---------------------------------+----------
pre trans'n 2 vocatio |        NA         NA         NA |        NA 
                      |     30.77      65.38       3.85 |    100.00 
                      |      0.50       0.26       0.06 |      0.26 
----------------------+---------------------------------+----------
pre trans'n 2 other n |       210      1,184        531 |     1,925 
                      |     10.91      61.51      27.58 |    100.00 
                      |     13.05      18.33      29.53 |     19.51 
----------------------+---------------------------------+----------
pre trans'n to unem/A |        NA         NA         NA |       138 
                      |     14.49      66.67      18.84 |    100.00 
                      |      1.24       1.42       1.45 |      1.40 
----------------------+---------------------------------+----------
pre trans'n 2 unemp/A |        NA        267         NA |       366 
                      |     14.21      72.95      12.84 |    100.00 
                      |      3.23       4.13       2.61 |      3.71 
----------------------+---------------------------------+----------
pre trans'n 2 unemp/A |        NA        100         NA |       142 
                      |     14.08      70.42      15.49 |    100.00 
                      |      1.24       1.55       1.22 |      1.44 
----------------------+---------------------------------+----------
pre trans'n 2 black h |     1,206      3,724        709 |     5,639 
                      |     21.39      66.04      12.57 |    100.00 
                      |     74.95      57.66      39.43 |     57.16 
----------------------+---------------------------------+----------
                Total |     1,609      6,459      1,798 |     9,866 
                      |     16.31      65.47      18.22 |    100.00 
                      |    100.00     100.00     100.00 |    100.00 
r; t=0.51 14:44:19

. 
.                 *  post-lignite destinations - by decade & experience *
.                 *
.                 tab pretrans exp_cat if pretrans>0, column row

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                      |        exp_cat
             pretrans |         1          2 |     Total
----------------------+----------------------+----------
pre retirement (witho |     2,776      6,667 |     9,443 
                      |     29.40      70.60 |    100.00 
                      |      1.28       6.56 |      2.97 
----------------------+----------------------+----------
pre trans'n 2 vocatio |       102         NA |       159 
                      |     64.15      35.85 |    100.00 
                      |      0.05       0.06 |      0.05 
----------------------+----------------------+----------
pre trans'n 2 other n |    29,292     10,701 |    39,993 
                      |     73.24      26.76 |    100.00 
                      |     13.51      10.52 |     12.56 
----------------------+----------------------+----------
pre trans'n to unem/A |     6,194      8,515 |    14,709 
                      |     42.11      57.89 |    100.00 
                      |      2.86       8.37 |      4.62 
----------------------+----------------------+----------
pre trans'n 2 unemp/A |    16,711      5,562 |    22,273 
                      |     75.03      24.97 |    100.00 
                      |      7.71       5.47 |      6.99 
----------------------+----------------------+----------
pre trans'n 2 unemp/A |     3,051        822 |     3,873 
                      |     78.78      21.22 |    100.00 
                      |      1.41       0.81 |      1.22 
----------------------+----------------------+----------
pre trans'n 2 black h |    25,198     26,537 |    51,735 
                      |     48.71      51.29 |    100.00 
                      |     11.63      26.10 |     16.25 
----------------------+----------------------+----------
pre transition out of |   129,347     42,418 |   171,765 
                      |     75.30      24.70 |    100.00 
                      |     59.68      41.71 |     53.94 
----------------------+----------------------+----------
pre transition out of |     4,081        412 |     4,493 
                      |     90.83       9.17 |    100.00 
                      |      1.88       0.41 |      1.41 
----------------------+----------------------+----------
                Total |   216,752    101,691 |   318,443 
                      |     68.07      31.93 |    100.00 
                      |    100.00     100.00 |    100.00 
r; t=0.34 14:44:19

.                 disp("Transitions out of lignite in the 1970s and 1980s")
Transitions out of lignite in the 1970s and 1980s
r; t=0.00 14:44:19

.                 tab pretrans exp_cat if pretrans>0 & pretrans!=. & thisspelllignite == 1 & decades==1, column row

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                      |        exp_cat
             pretrans |         1          2 |     Total
----------------------+----------------------+----------
pre retirement (witho |     2,034      3,649 |     5,683 
                      |     35.79      64.21 |    100.00 
                      |     13.14      23.77 |     18.44 
----------------------+----------------------+----------
pre trans'n 2 vocatio |        NA         NA |        NA 
                      |     83.33      16.67 |    100.00 
                      |      0.32       0.07 |      0.19 
----------------------+----------------------+----------
pre trans'n 2 other n |     3,269      1,375 |     4,644 
                      |     70.39      29.61 |    100.00 
                      |     21.12       8.96 |     15.06 
----------------------+----------------------+----------
pre trans'n to unem/A |       609        761 |     1,370 
                      |     44.45      55.55 |    100.00 
                      |      3.94       4.96 |      4.44 
----------------------+----------------------+----------
pre trans'n 2 unemp/A |       345        523 |       868 
                      |     39.75      60.25 |    100.00 
                      |      2.23       3.41 |      2.82 
----------------------+----------------------+----------
pre trans'n 2 unemp/A |       244        105 |       349 
                      |     69.91      30.09 |    100.00 
                      |      1.58       0.68 |      1.13 
----------------------+----------------------+----------
pre trans'n 2 black h |     8,925      8,928 |    17,853 
                      |     49.99      50.01 |    100.00 
                      |     57.67      58.16 |     57.91 
----------------------+----------------------+----------
                Total |    15,476     15,351 |    30,827 
                      |     50.20      49.80 |    100.00 
                      |    100.00     100.00 |    100.00 
r; t=0.54 14:44:20

.                 disp("Transitions out of lignite in the 1990s")         
Transitions out of lignite in the 1990s
r; t=0.00 14:44:20

.                 tab pretrans  exp_cat if pretrans>0 & pretrans!=. & thisspelllignite == 1 & decades==2, column row

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                      |        exp_cat
             pretrans |         1          2 |     Total
----------------------+----------------------+----------
pre retirement (witho |       517        767 |     1,284 
                      |     40.26      59.74 |    100.00 
                      |      0.88       3.22 |      1.55 
----------------------+----------------------+----------
pre trans'n 2 vocatio |        NA         NA |        NA 
                      |     83.02      16.98 |    100.00 
                      |      0.07       0.04 |      0.06 
----------------------+----------------------+----------
pre trans'n 2 other n |    23,344      4,596 |    27,940 
                      |     83.55      16.45 |    100.00 
                      |     39.70      19.31 |     33.82 
----------------------+----------------------+----------
pre trans'n to unem/A |     4,910      5,936 |    10,846 
                      |     45.27      54.73 |    100.00 
                      |      8.35      24.94 |     13.13 
----------------------+----------------------+----------
pre trans'n 2 unemp/A |    14,645      3,897 |    18,542 
                      |     78.98      21.02 |    100.00 
                      |     24.91      16.37 |     22.45 
----------------------+----------------------+----------
pre trans'n 2 unemp/A |     2,418        281 |     2,699 
                      |     89.59      10.41 |    100.00 
                      |      4.11       1.18 |      3.27 
----------------------+----------------------+----------
pre trans'n 2 black h |    12,923      8,318 |    21,241 
                      |     60.84      39.16 |    100.00 
                      |     21.98      34.94 |     25.71 
----------------------+----------------------+----------
                Total |    58,801     23,804 |    82,605 
                      |     71.18      28.82 |    100.00 
                      |    100.00     100.00 |    100.00 
r; t=0.57 14:44:20

.                 disp("Transitions out of lignite in the 2000s")
Transitions out of lignite in the 2000s
r; t=0.00 14:44:20

.                 tab pretrans  exp_cat if pretrans>0 & pretrans!=. & thisspelllignite == 1 & decades==3, column row

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                      |        exp_cat
             pretrans |         1          2 |     Total
----------------------+----------------------+----------
pre retirement (witho |        NA        543 |       596 
                      |      8.89      91.11 |    100.00 
                      |      0.83       4.52 |      3.24 
----------------------+----------------------+----------
pre trans'n 2 vocatio |        NA         NA |        NA 
                      |     25.00      75.00 |    100.00 
                      |      0.08       0.12 |      0.11 
----------------------+----------------------+----------
pre trans'n 2 other n |     1,921      3,558 |     5,479 
                      |     35.06      64.94 |    100.00 
                      |     30.05      29.63 |     29.78 
----------------------+----------------------+----------
pre trans'n to unem/A |       644      1,709 |     2,353 
                      |     27.37      72.63 |    100.00 
                      |     10.07      14.23 |     12.79 
----------------------+----------------------+----------
pre trans'n 2 unemp/A |     1,547        947 |     2,494 
                      |     62.03      37.97 |    100.00 
                      |     24.20       7.89 |     13.55 
----------------------+----------------------+----------
pre trans'n 2 unemp/A |       332        347 |       679 
                      |     48.90      51.10 |    100.00 
                      |      5.19       2.89 |      3.69 
----------------------+----------------------+----------
pre trans'n 2 black h |     1,891      4,888 |     6,779 
                      |     27.89      72.11 |    100.00 
                      |     29.58      40.71 |     36.84 
----------------------+----------------------+----------
                Total |     6,393     12,007 |    18,400 
                      |     34.74      65.26 |    100.00 
                      |    100.00     100.00 |    100.00 
r; t=0.52 14:44:21

.                 disp("Transitions out of lignite in the 2010s")
Transitions out of lignite in the 2010s
r; t=0.00 14:44:21

.                 tab pretrans  exp_cat if pretrans>0 & pretrans!=. & thisspelllignite == 1 & decades==4, column row              

+-------------------+
| Key               |
|-------------------|
|     frequency     |
|  row percentage   |
| column percentage |
+-------------------+

                      |        exp_cat
             pretrans |         1          2 |     Total
----------------------+----------------------+----------
pre retirement (witho |       172      1,708 |     1,880 
                      |      9.15      90.85 |    100.00 
                      |      6.48      22.18 |     18.16 
----------------------+----------------------+----------
pre trans'n 2 vocatio |        NA         NA |        NA 
                      |     11.54      88.46 |    100.00 
                      |      0.11       0.30 |      0.25 
----------------------+----------------------+----------
pre trans'n 2 other n |       758      1,172 |     1,930 
                      |     39.27      60.73 |    100.00 
                      |     28.56      15.22 |     18.64 
----------------------+----------------------+----------
pre trans'n to unem/A |        NA        109 |       140 
                      |     22.14      77.86 |    100.00 
                      |      1.17       1.42 |      1.35 
----------------------+----------------------+----------
pre trans'n 2 unemp/A |       174        195 |       369 
                      |     47.15      52.85 |    100.00 
                      |      6.56       2.53 |      3.56 
----------------------+----------------------+----------
pre trans'n 2 unemp/A |        NA         NA |       146 
                      |     39.04      60.96 |    100.00 
                      |      2.15       1.16 |      1.41 
----------------------+----------------------+----------
pre trans'n 2 black h |     1,459      4,403 |     5,862 
                      |     24.89      75.11 |    100.00 
                      |     54.97      57.19 |     56.62 
----------------------+----------------------+----------
                Total |     2,654      7,699 |    10,353 
                      |     25.64      74.36 |    100.00 
                      |    100.00     100.00 |    100.00 
r; t=0.53 14:44:21

.         
.                 **********************
. 
.                 
.         ************************************************
.         *** (2.1) Direct job-to-job transitions    *****
.         ************************************************
.         * (previously section 2a in 4transitions)
. 
.         * only relatively few people changed sector but remained in same firm (these may be dodgy)
.         count           if pretrans[_n-1]==3 & pid==pid[_n-1] & betnr==betnr[_n-1] 
  104
r; t=0.52 14:44:22

. 
.         * (2.1.a) Distribution of post-transition industries
.         * 
.         *** Which industries / sectors do people join after direct job-to-job moves?
.         *** NB. relevant for JAERE revision
. 
.         tab wirtschaftszweige if posttrans ==3 & thisspelllignite == 1, sort 
no observations
r; t=0.32 14:44:22

.         bys decades:    tab wirtschaftszweige if posttrans ==3 & thisspelllignite == 1, sort

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> decades = 1970s-1980s
no observations

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> decades = 1990s
no observations

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> decades = 2000s
no observations

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> decades = 2010s
no observations

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> decades = .
no observations

r; t=1.00 14:44:23

.         di "Destination industries of direct J2J lignite-leavers all areas by decades"
Destination industries of direct J2J lignite-leavers all areas by decades
r; t=0.00 14:44:23

.         forvalues t = 1/4 {
  2.                 cap tab wirtschaftszweige if posttrans == 3 & decades == `t' 
  3.                 cap estpost tab wirtschaftszweige if posttrans == 3 & decades == `t', sort
  4.                 cap estimates store y`t'
  5.         }
r; t=2.79 14:44:26

.         esttab y* using results/${samplefolder}/5inddec.csv, label plain replace
(file results/two/5inddec.csv not found)
(output written to results/two/5inddec.csv)
r; t=0.20 14:44:26

.         estimates clear
r; t=0.13 14:44:26

. 
.         *** Differences across mining areas ***
.         di "Destination industries of lignite-leavers across mining areas by decades"
Destination industries of lignite-leavers across mining areas by decades
r; t=0.00 14:44:26

.         forvalues i = 1/5 {
  2.                 forvalues t = 1/4 {
  3.                         cap tab wirtschaftszweige if posttrans == 3 & decades == `t' & mining_area == `i'
  4.                         cap estpost tab wirtschaftszweige if posttrans == 3 & decades == `t' & mining_area == `i'
  5.                         cap estimates store y`t'
  6.                 }
  7.                 esttab y* using results/${samplefolder}/5inddecarea`i'.csv, label plain replace
  8.                 estimates clear
  9.         }
(file results/two/5inddecarea1.csv not found)
(output written to results/two/5inddecarea1.csv)
(file results/two/5inddecarea2.csv not found)
(output written to results/two/5inddecarea2.csv)
(file results/two/5inddecarea3.csv not found)
(output written to results/two/5inddecarea3.csv)
(file results/two/5inddecarea4.csv not found)
(output written to results/two/5inddecarea4.csv)
(file results/two/5inddecarea5.csv not found)
(output written to results/two/5inddecarea5.csv)
r; t=16.44 14:44:43

.         
.         *** Differences across other characteristics (2010-2017) ***
.         bys frau:               tab wirtschaftszweige if posttrans ==3 & decades==4 & thisspelllignite == 1

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> frau = mann
no observations

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> frau = frau
no observations

r; t=1.01 14:44:44

.         bys exp_cat:    tab wirtschaftszweige if posttrans ==3 & decades==4 & thisspelllignite == 1

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> exp_cat = 1
no observations

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> exp_cat = 2
no observations

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> exp_cat = .
no observations

r; t=1.05 14:44:45

.         bys ageendcat:  tab wirtschaftszweige if posttrans ==3 & decades==4 & thisspelllignite == 1

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> ageendcat = 18-30 years old
no observations

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> ageendcat = 30-50 years old
no observations

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> ageendcat = over 50 years old
no observations

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> ageendcat = .
no observations

r; t=1.01 14:44:46

.         bys bild:               tab wirtschaftszweige if posttrans ==3 & decades==4 & thisspelllignite == 1

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> bild = 1 no vocational training
no observations

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> bild = 2 vocational training
no observations

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> bild = 3 university or university of applied science
no observations

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> bild = .
no observations

r; t=1.12 14:44:47

. 
.         *** Differences across other characteristics (post-2000) ***
.         bys frau:               tab wirtschaftszweige if posttrans ==3 & (decades==3 | decades==4) & thisspelllignite == 1

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> frau = mann
no observations

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> frau = frau
no observations

r; t=1.14 14:44:48

.         bys exp_cat:    tab wirtschaftszweige if posttrans ==3 & (decades==3 | decades==4) & thisspelllignite == 1

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> exp_cat = 1
no observations

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> exp_cat = 2
no observations

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> exp_cat = .
no observations

r; t=1.21 14:44:49

.         bys ageendcat:  tab wirtschaftszweige if posttrans ==3 & (decades==3 | decades==4) & thisspelllignite == 1

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> ageendcat = 18-30 years old
no observations

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> ageendcat = 30-50 years old
no observations

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> ageendcat = over 50 years old
no observations

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> ageendcat = .
no observations

r; t=1.10 14:44:50

.         bys bild:               tab wirtschaftszweige if posttrans ==3 & (decades==3 | decades==4) & thisspelllignite == 1

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> bild = 1 no vocational training
no observations

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> bild = 2 vocational training
no observations

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> bild = 3 university or university of applied science
no observations

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> bild = .
no observations

r; t=1.21 14:44:52

. 
.         ************************************************************
.         *** (2.2) including indirect job-to-job transitions    *****
.         ************************************************************
.         * including indirect job-to-job transitions out of lignite (7 & 8) as well as direct ones (3)
. 
.         g jposttrans=.
(1,456,038 missing values generated)
r; t=0.04 14:44:52

.         bysort pid (begepi): replace jposttrans = 1 if (pretrans[_n-2]==7)      
(22273 real changes made)
r; t=0.72 14:44:52

.         
.         
.         di "Destination indu of direct & indirect J2J out of lignite, all areas, by decades"
Destination indu of direct & indirect J2J out of lignite, all areas, by decades
r; t=0.00 14:44:52

.                                 tab wirtschaftszweige if (posttrans ==3 | jposttrans==1), sort 

                      wirtschaftszweige |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                             Baugewerbe |     11,667       18.74       18.74
Erbringung_v_freiberuflichen_und_techni |     10,494       16.85       35.59
Wasserversorgung;_Abwasser-_und_Abfalle |      5,569        8.94       44.53
Erbringung_v_sonstigen_wirtschaftlichen |      5,224        8.39       52.92
                      Energieversorgung |      4,842        7.78       60.70
Handel;_Instandhaltung_und_Reperatur_v_ |      3,162        5.08       65.78
      Kokerei_und_Mineralölverarbeitung |      2,381        3.82       69.60
Metallerzeugung_und_-bearbeitung,_Herst |      2,188        3.51       73.12
Öffentliche_Verwaltung,_Verteidigung;_S |      2,163        3.47       76.59
                    Verkehr_und_Lagerei |      1,672        2.69       79.28
      Bergbau_Gewinning_v_Steinen_Erden |      1,513        2.43       81.71
Sonstige_Herstellung_v_Waren,_Reperatur |      1,462        2.35       84.05
               Erziehung_und_Unterricht |      1,265        2.03       86.09
              Sonstige_Dienstleistungen |      1,072        1.72       87.81
                           Maschinenbau |      1,009        1.62       89.43
Herstellung_v_Gummi_und_Kunststoffwaren |        860        1.38       90.81
                  Heime_und_Sozialwesen |        741        1.19       92.00
                            Gastgewerbe |        622        1.00       93.00
Landwirtschaft_Forstwirtschaft_Fischere |        498        0.80       93.80
                            Fahrzeugbau |        418        0.67       94.47
         Grundstücks-_und_Wohnungswesen |        397        0.64       95.11
Herstellung_v_Nahrungs-_und_Genussmitte |        395        0.63       95.74
Herstellung_v_elektrischen_Ausrüstungen |        380        0.61       96.35
                       Gesundheitswesen |        320        0.51       96.87
Herstellung_v_Holzwaren,_Papier,_Pappe_ |        315        0.51       97.37
Informationstechnologische_und_Informat |        298        0.48       97.85
  Herstellung_v_chemischen_Erzeugnissen |        252        0.40       98.25
Herstellung_v_Datenverarbeitungsgeräten |        250        0.40       98.66
                    Kunst,_Unterhaltung |        192        0.31       98.96
Wissenschaftliche_Forschung_und_Entwick |        156        0.25       99.21
                            Undefiniert |        137        0.22       99.43
Erbringung_v_Finanz-_und_Verkehrsdienst |         NA        0.12       99.56
Sonstige_freiberufliche,_wissenschaftli |         NA        0.11       99.67
Verlagswesen,_audiovisuelle_Medien_und_ |         NA        0.11       99.78
Herstellung_v_Textilien,_Bekleidung,_Le |         NA        0.09       99.87
                      Telekommunikation |         NA        0.07       99.93
Herstellung_v_pharmazeutischen_Erzeugni |         NA        0.05       99.99
Private_Haushalte_mit_Hauspersonal_Haus |         NA        0.01      100.00
Exterritoriale_Organisationen_und_Körpe |         NA        0.00      100.00
----------------------------------------+-----------------------------------
                                  Total |     62,266      100.00
r; t=0.36 14:44:53

.         * for JAERE revision: focus on the most recent decade
.         bys decades:    tab wirtschaftszweige if (posttrans ==3 | jposttrans==1), sort

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> decades = 1970s-1980s

                      wirtschaftszweige |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
      Kokerei_und_Mineralölverarbeitung |      1,700       44.16       44.16
                      Energieversorgung |        327        8.49       52.65
                             Baugewerbe |        306        7.95       60.60
Metallerzeugung_und_-bearbeitung,_Herst |        164        4.26       64.86
Handel;_Instandhaltung_und_Reperatur_v_ |        156        4.05       68.91
                    Verkehr_und_Lagerei |        116        3.01       71.92
Öffentliche_Verwaltung,_Verteidigung;_S |        116        3.01       74.94
      Bergbau_Gewinning_v_Steinen_Erden |        107        2.78       77.71
Herstellung_v_Gummi_und_Kunststoffwaren |        103        2.68       80.39
                           Maschinenbau |        101        2.62       83.01
Erbringung_v_sonstigen_wirtschaftlichen |         NA        2.10       85.12
Erbringung_v_freiberuflichen_und_techni |         NA        2.05       87.17
                            Fahrzeugbau |         NA        2.00       89.17
Landwirtschaft_Forstwirtschaft_Fischere |         NA        1.64       90.81
Herstellung_v_Nahrungs-_und_Genussmitte |         NA        1.06       91.87
Herstellung_v_Holzwaren,_Papier,_Pappe_ |         NA        1.06       92.94
Herstellung_v_elektrischen_Ausrüstungen |         NA        0.75       93.69
  Herstellung_v_chemischen_Erzeugnissen |         NA        0.70       94.39
Herstellung_v_Datenverarbeitungsgeräten |         NA        0.65       95.04
              Sonstige_Dienstleistungen |         NA        0.60       95.64
                            Undefiniert |         NA        0.52       96.16
               Erziehung_und_Unterricht |         NA        0.49       96.65
                       Gesundheitswesen |         NA        0.49       97.14
Sonstige_Herstellung_v_Waren,_Reperatur |         NA        0.47       97.61
Erbringung_v_Finanz-_und_Verkehrsdienst |         NA        0.31       97.92
                            Gastgewerbe |         NA        0.29       98.21
Herstellung_v_pharmazeutischen_Erzeugni |         NA        0.26       98.47
                  Heime_und_Sozialwesen |         NA        0.26       98.73
Herstellung_v_Textilien,_Bekleidung,_Le |         NA        0.23       98.96
Wasserversorgung;_Abwasser-_und_Abfalle |         NA        0.23       99.19
Verlagswesen,_audiovisuelle_Medien_und_ |         NA        0.23       99.43
                    Kunst,_Unterhaltung |         NA        0.16       99.58
Informationstechnologische_und_Informat |         NA        0.10       99.69
Wissenschaftliche_Forschung_und_Entwick |         NA        0.10       99.79
         Grundstücks-_und_Wohnungswesen |         NA        0.08       99.87
Sonstige_freiberufliche,_wissenschaftli |         NA        0.08       99.95
                      Telekommunikation |         NA        0.03       99.97
Exterritoriale_Organisationen_und_Körpe |         NA        0.03      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,850      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> decades = 1990s

                      wirtschaftszweige |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
Erbringung_v_freiberuflichen_und_techni |      9,160       23.83       23.83
                             Baugewerbe |      8,761       22.79       46.63
Wasserversorgung;_Abwasser-_und_Abfalle |      3,701        9.63       56.25
Erbringung_v_sonstigen_wirtschaftlichen |      3,023        7.87       64.12
Handel;_Instandhaltung_und_Reperatur_v_ |      1,982        5.16       69.28
                      Energieversorgung |      1,531        3.98       73.26
Öffentliche_Verwaltung,_Verteidigung;_S |      1,472        3.83       77.09
      Bergbau_Gewinning_v_Steinen_Erden |      1,167        3.04       80.13
                    Verkehr_und_Lagerei |        967        2.52       82.64
Metallerzeugung_und_-bearbeitung,_Herst |        959        2.50       85.14
Sonstige_Herstellung_v_Waren,_Reperatur |        850        2.21       87.35
              Sonstige_Dienstleistungen |        590        1.54       88.88
                            Gastgewerbe |        449        1.17       90.05
Herstellung_v_Gummi_und_Kunststoffwaren |        445        1.16       91.21
                  Heime_und_Sozialwesen |        394        1.03       92.23
                           Maschinenbau |        389        1.01       93.25
               Erziehung_und_Unterricht |        380        0.99       94.23
      Kokerei_und_Mineralölverarbeitung |        373        0.97       95.21
Landwirtschaft_Forstwirtschaft_Fischere |        288        0.75       95.95
Herstellung_v_Nahrungs-_und_Genussmitte |        201        0.52       96.48
Herstellung_v_elektrischen_Ausrüstungen |        200        0.52       97.00
                       Gesundheitswesen |        136        0.35       97.35
         Grundstücks-_und_Wohnungswesen |        130        0.34       97.69
Herstellung_v_Datenverarbeitungsgeräten |        128        0.33       98.02
Herstellung_v_Holzwaren,_Papier,_Pappe_ |        123        0.32       98.34
                    Kunst,_Unterhaltung |        104        0.27       98.61
                            Fahrzeugbau |        100        0.26       98.87
                            Undefiniert |         NA        0.21       99.08
Wissenschaftliche_Forschung_und_Entwick |         NA        0.21       99.28
  Herstellung_v_chemischen_Erzeugnissen |         NA        0.18       99.46
Informationstechnologische_und_Informat |         NA        0.15       99.61
Sonstige_freiberufliche,_wissenschaftli |         NA        0.09       99.70
Erbringung_v_Finanz-_und_Verkehrsdienst |         NA        0.08       99.78
Verlagswesen,_audiovisuelle_Medien_und_ |         NA        0.07       99.85
Herstellung_v_Textilien,_Bekleidung,_Le |         NA        0.07       99.92
                      Telekommunikation |         NA        0.06       99.98
Herstellung_v_pharmazeutischen_Erzeugni |         NA        0.02       99.99
Private_Haushalte_mit_Hauspersonal_Haus |         NA        0.01      100.00
----------------------------------------+-----------------------------------
                                  Total |     38,436      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> decades = 2000s

                      wirtschaftszweige |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                             Baugewerbe |      2,294       16.22       16.22
                      Energieversorgung |      1,622       11.47       27.69
Erbringung_v_sonstigen_wirtschaftlichen |      1,547       10.94       38.63
Wasserversorgung;_Abwasser-_und_Abfalle |      1,511       10.69       49.32
               Erziehung_und_Unterricht |        826        5.84       55.16
Erbringung_v_freiberuflichen_und_techni |        792        5.60       60.76
Handel;_Instandhaltung_und_Reperatur_v_ |        757        5.35       66.11
Metallerzeugung_und_-bearbeitung,_Herst |        516        3.65       69.76
Sonstige_Herstellung_v_Waren,_Reperatur |        460        3.25       73.01
Öffentliche_Verwaltung,_Verteidigung;_S |        442        3.13       76.14
                    Verkehr_und_Lagerei |        430        3.04       79.18
              Sonstige_Dienstleistungen |        394        2.79       81.97
                           Maschinenbau |        305        2.16       84.12
                  Heime_und_Sozialwesen |        263        1.86       85.98
Herstellung_v_Gummi_und_Kunststoffwaren |        205        1.45       87.43
Informationstechnologische_und_Informat |        202        1.43       88.86
      Kokerei_und_Mineralölverarbeitung |        148        1.05       89.91
                            Fahrzeugbau |        138        0.98       90.88
         Grundstücks-_und_Wohnungswesen |        126        0.89       91.78
      Bergbau_Gewinning_v_Steinen_Erden |        124        0.88       92.65
Landwirtschaft_Forstwirtschaft_Fischere |        109        0.77       93.42
                       Gesundheitswesen |        107        0.76       94.18
Herstellung_v_elektrischen_Ausrüstungen |        106        0.75       94.93
Herstellung_v_Nahrungs-_und_Genussmitte |         NA        0.70       95.63
                            Gastgewerbe |         NA        0.70       96.33
Herstellung_v_Holzwaren,_Papier,_Pappe_ |         NA        0.68       97.01
  Herstellung_v_chemischen_Erzeugnissen |         NA        0.64       97.65
Herstellung_v_Datenverarbeitungsgeräten |         NA        0.50       98.15
                    Kunst,_Unterhaltung |         NA        0.47       98.61
Wissenschaftliche_Forschung_und_Entwick |         NA        0.43       99.05
                            Undefiniert |         NA        0.20       99.24
Sonstige_freiberufliche,_wissenschaftli |         NA        0.16       99.41
Erbringung_v_Finanz-_und_Verkehrsdienst |         NA        0.16       99.56
Verlagswesen,_audiovisuelle_Medien_und_ |         NA        0.13       99.70
                      Telekommunikation |         NA        0.12       99.82
Herstellung_v_Textilien,_Bekleidung,_Le |         NA        0.11       99.92
Herstellung_v_pharmazeutischen_Erzeugni |         NA        0.05       99.97
Private_Haushalte_mit_Hauspersonal_Haus |         NA        0.03      100.00
----------------------------------------+-----------------------------------
                                  Total |     14,141      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> decades = 2010s

                      wirtschaftszweige |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                      Energieversorgung |      1,362       23.33       23.33
Erbringung_v_sonstigen_wirtschaftlichen |        573        9.81       33.14
Metallerzeugung_und_-bearbeitung,_Herst |        549        9.40       42.54
Erbringung_v_freiberuflichen_und_techni |        463        7.93       50.47
Wasserversorgung;_Abwasser-_und_Abfalle |        348        5.96       56.43
                             Baugewerbe |        306        5.24       61.67
Handel;_Instandhaltung_und_Reperatur_v_ |        267        4.57       66.24
                           Maschinenbau |        214        3.67       69.91
      Kokerei_und_Mineralölverarbeitung |        160        2.74       72.65
                    Verkehr_und_Lagerei |        159        2.72       75.37
         Grundstücks-_und_Wohnungswesen |        138        2.36       77.74
Sonstige_Herstellung_v_Waren,_Reperatur |        134        2.29       80.03
Öffentliche_Verwaltung,_Verteidigung;_S |        133        2.28       82.31
      Bergbau_Gewinning_v_Steinen_Erden |        115        1.97       84.28
Herstellung_v_Gummi_und_Kunststoffwaren |        107        1.83       86.11
                            Fahrzeugbau |        103        1.76       87.87
                  Heime_und_Sozialwesen |         NA        1.27       89.14
  Herstellung_v_chemischen_Erzeugnissen |         NA        1.13       90.27
              Sonstige_Dienstleistungen |         NA        1.11       91.39
                            Gastgewerbe |         NA        1.08       92.46
                       Gesundheitswesen |         NA        0.99       93.46
Herstellung_v_Holzwaren,_Papier,_Pappe_ |         NA        0.94       94.40
Herstellung_v_Nahrungs-_und_Genussmitte |         NA        0.92       95.32
Herstellung_v_elektrischen_Ausrüstungen |         NA        0.77       96.10
               Erziehung_und_Unterricht |         NA        0.69       96.78
Landwirtschaft_Forstwirtschaft_Fischere |         NA        0.65       97.43
Informationstechnologische_und_Informat |         NA        0.62       98.05
Herstellung_v_Datenverarbeitungsgeräten |         NA        0.45       98.49
                    Kunst,_Unterhaltung |         NA        0.27       98.77
Verlagswesen,_audiovisuelle_Medien_und_ |         NA        0.22       98.99
Wissenschaftliche_Forschung_und_Entwick |         NA        0.21       99.20
                            Undefiniert |         NA        0.17       99.37
Herstellung_v_pharmazeutischen_Erzeugni |         NA        0.17       99.54
Sonstige_freiberufliche,_wissenschaftli |         NA        0.17       99.71
Erbringung_v_Finanz-_und_Verkehrsdienst |         NA        0.15       99.86
Herstellung_v_Textilien,_Bekleidung,_Le |         NA        0.10       99.97
                      Telekommunikation |         NA        0.03      100.00
----------------------------------------+-----------------------------------
                                  Total |      5,839      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> decades = .
no observations

r; t=1.17 14:44:54

. 
.         forvalues t = 1/4 {
  2.                 cap tab wirtschaftszweige if (posttrans == 3 | jposttrans==1) & decades==`t', sort
  3.                 cap estpost tab wirtschaftszweige if (posttrans == 3 | jposttrans==1) & decades == `t'
  4.                 cap estimates store y`t'
  5.         }
r; t=3.11 14:44:57

.         esttab y* using results/${samplefolder}/5inddec2.csv, label plain replace
(file results/two/5inddec2.csv not found)
(output written to results/two/5inddec2.csv)
r; t=0.17 14:44:57

.         estimates clear
r; t=0.01 14:44:57

. 
.         **************************************************************
.         *** (2.3) focus on workers who find job after unemployment ***
.         **************************************************************          
.                 /*
>                 * Log-file of this part - now commented out - is available in log-file of 25th May 2021
>                 *** Compare wage distributions before and after j2j-transition
>                 sum suminc_R if pretrans[_n] == 3 & statsimple[_n]==1, d
>                 sum suminc_R if pretrans[_n-1] ==3 & statsimple[_n-1]==1 & pid==pid[_n-1], d
>                 *g lsuminc_R=log(suminc_R)
>                 *kdensity lsuminc_R if pretrans[_n] == 3 & statsimple[_n]==1 , adpplot(kdensity lsuminc_R if pretrans[_n-1]==3 & statsimple[_n-1]==1)
>                 
>                 // average difference of wage before and after
>                 gen wagediff = suminc_R[_n] - suminc_R[_n+1] if pretrans[_n]==3 & pid==pid[_n+1]
>                 sum wagediff, d
>         
>                 *g lwagediff=log(wagediff)
>                 *kdensity lwagediff
>         
>                 // dummy (=1 if wage BEFORE transition is higher)
>                 cap drop higherwage
>                 gen higherwage=.
>                 replace higherwage = 0 if wagediff<0
>                 replace higherwage = 1 if wagediff>0 & wagediff!=.
>                 tab higherwage          
>                 */
. 
.         ****************************************************************************
.         *** (2.4) geographic distribution of destinations after transitions    *****
.         ****************************************************************************
.                 
.         * geographic locations prior to transitions
.         *
.         gen pre_wo_bula = wo_bula if (pretrans==3 | pretrans==7) 
(1,393,772 missing values generated)
r; t=0.06 14:44:57

.         gen pre_wo_kreis = wo_kreis if (pretrans==3 | pretrans==7) 
(1,393,772 missing values generated)
r; t=0.06 14:44:57

. 
.         gen pre_ao_bula = ao_bula if (pretrans==3 | pretrans==7)  
(1,393,772 missing values generated)
r; t=0.05 14:44:57

.         gen pre_ao_kreis = ao_kreis if (pretrans==3 | pretrans==7) 
(1,393,772 missing values generated)
r; t=0.07 14:44:57

.                 
.         * copy later geolocations to prior observations to allow for matrix
.         * create "future geographic locations"
.         
.         bys pid (begepi): gen fut_wo_bula = wo_bula[_n+1] if pretrans==3
(1,416,045 missing values generated)
r; t=0.75 14:44:58

.         bys pid (begepi): gen fut_ao_bula = ao_bula[_n+1] if pretrans==3 
(1,416,045 missing values generated)
r; t=0.06 14:44:58

.         bys pid (begepi): gen fut_wo_kreis = wo_kreis[_n+1] if pretrans==3 
(1,416,045 missing values generated)
r; t=0.07 14:44:58

.         bys pid (begepi): gen fut_ao_kreis = ao_kreis[_n+1] if pretrans==3 
(1,416,045 missing values generated)
r; t=0.06 14:44:58

. 
.         bys pid (begepi): replace fut_wo_bula = wo_bula[_n+2] if pretrans==7
(22273 real changes made)
r; t=0.05 14:44:58

.         bys pid (begepi): replace fut_ao_bula = ao_bula[_n+2] if pretrans==7
(22273 real changes made)
r; t=0.06 14:44:58

.         bys pid (begepi): replace fut_wo_kreis = wo_kreis[_n+2] if pretrans==7 
(22273 real changes made)
r; t=0.06 14:44:59

.         bys pid (begepi): replace fut_ao_kreis = ao_kreis[_n+2] if pretrans==7 
(22273 real changes made)
r; t=0.06 14:44:59

. 
. 
.         * Matrices of pre and post transition geographic location
.         tab pre_ao_bula fut_ao_bula

NA

.         * where do people move to - by mining area
.         tab fut_ao_bula if (pretrans==3 | pretrans==7)                                                  


fut_ao_bula |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         NA        0.02        0.02
          1 |         NA        0.09        0.10
          2 |        353        0.57        0.67
          3 |      1,809        2.91        3.58
          4 |         NA        0.03        3.61
          5 |      9,465       15.20       18.81
          6 |        922        1.48       20.29
          7 |        127        0.20       20.49
          8 |        476        0.76       21.26
          9 |      1,430        2.30       23.56
         10 |        148        0.24       23.79
         11 |        688        1.10       24.90
         12 |     21,584       34.66       59.56
         13 |         NA        0.11       59.67
         14 |     13,911       22.34       82.02
         15 |      9,542       15.32       97.34
         16 |      1,656        2.66      100.00
------------+-----------------------------------
      Total |     62,266      100.00
r; t=0.34 14:44:59

.         bys mining_area: tab fut_ao_bula if (pretrans==3 | pretrans==7)                 

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> mining_area = Lausitzer Revier

fut_ao_bula |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         NA        0.02        0.02
          1 |         NA        0.06        0.08
          2 |        305        1.03        1.11
          3 |        132        0.45        1.56
          4 |         NA        0.02        1.58
          5 |        269        0.91        2.49
          6 |         NA        0.29        2.79
          7 |         NA        0.15        2.94
          8 |        259        0.88        3.82
          9 |        306        1.04        4.85
         10 |         NA        0.12        4.97
         11 |        545        1.85        6.82
         12 |     20,273       68.70       75.52
         13 |         NA        0.12       75.64
         14 |      6,669       22.60       98.24
         15 |        442        1.50       99.74
         16 |         NA        0.26      100.00
------------+-----------------------------------
      Total |     29,508      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> mining_area = Mitteldt. Revier

fut_ao_bula |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         NA        0.01        0.01
          1 |         NA        0.09        0.10
          2 |         NA        0.07        0.18
          3 |        149        0.73        0.91
          4 |         NA        0.02        0.94
          5 |        243        1.20        2.13
          6 |        117        0.58        2.71
          7 |         NA        0.21        2.92
          8 |        155        0.76        3.68
          9 |        344        1.69        5.37
         10 |         NA        0.09        5.46
         11 |        110        0.54        6.00
         12 |      1,264        6.22       12.23
         13 |         NA        0.13       12.36
         14 |      7,218       35.54       47.90
         15 |      9,014       44.39       92.29
         16 |      1,566        7.71      100.00
------------+-----------------------------------
      Total |     20,307      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> mining_area = Helmstedter Revier

fut_ao_bula |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         NA        0.36        0.36
          2 |         NA        0.71        1.07
          3 |      1,427       84.84       85.91
          4 |         NA        0.18       86.09
          5 |         NA        2.56       88.64
          6 |         NA        2.68       91.32
          7 |         NA        0.06       91.38
          8 |         NA        1.13       92.51
          9 |         NA        1.78       94.29
         10 |         NA        0.12       94.41
         11 |         NA        0.42       94.83
         12 |         NA        0.65       95.48
         13 |         NA        0.24       95.72
         14 |         NA        0.30       96.02
         15 |         NA        3.86       99.88
         16 |         NA        0.12      100.00
------------+-----------------------------------
      Total |      1,682      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> mining_area = Rheinisches Revier

fut_ao_bula |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         NA        0.02        0.02
          1 |         NA        0.13        0.15
          2 |         NA        0.15        0.31
          3 |         NA        0.42        0.73
          4 |         NA        0.02        0.75
          5 |      8,804       97.17       97.92
          6 |         NA        0.51       98.43
          7 |         NA        0.34       98.77
          8 |         NA        0.35       99.13
          9 |         NA        0.45       99.58
         10 |         NA        0.08       99.66
         11 |         NA        0.09       99.75
         12 |         NA        0.08       99.82
         13 |         NA        0.01       99.83
         14 |         NA        0.09       99.92
         15 |         NA        0.06       99.98
         16 |         NA        0.02      100.00
------------+-----------------------------------
      Total |      9,060      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> mining_area = Other Reviere

fut_ao_bula |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |         NA        0.06        0.06
          2 |         NA        0.41        0.47
          3 |         NA        3.70        4.17
          4 |         NA        0.23        4.40
          5 |        106        6.22       10.62
          6 |        627       36.80       47.42
          7 |         NA        0.41       47.83
          8 |         NA        0.65       48.47
          9 |        709       41.61       90.08
         10 |         NA        5.05       95.13
         11 |         NA        1.06       96.19
         12 |         NA        1.70       97.89
         13 |         NA        0.18       98.06
         14 |         NA        0.65       98.71
         15 |         NA        0.94       99.65
         16 |         NA        0.35      100.00
------------+-----------------------------------
      Total |      1,704      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> mining_area = .

fut_ao_bula |      Freq.     Percent        Cum.
------------+-----------------------------------
          7 |         NA       20.00       20.00
         16 |         NA       80.00      100.00
------------+-----------------------------------
      Total |         NA      100.00

r; t=1.29 14:45:01

.         bys mining_area: tab fut_ao_kreis if (pretrans==3 | pretrans==7), sort  

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> mining_area = Lausitzer Revier

fut_ao_krei |
          s |      Freq.     Percent        Cum.
------------+-----------------------------------
      12066 |     13,370       45.31       45.31
      12071 |      4,813       16.31       61.62
      14625 |      3,884       13.16       74.78
      14626 |      1,769        5.99       80.78
      12052 |      1,239        4.20       84.98
      11000 |        545        1.85       86.82
      14612 |        306        1.04       87.86
       2000 |        305        1.03       88.89
      12062 |        291        0.99       89.88
      12061 |        189        0.64       90.52
      14713 |        163        0.55       91.07
      14729 |        141        0.48       91.55
      15088 |        131        0.44       92.00
      12067 |        118        0.40       92.40
      15082 |        103        0.35       92.74
      12054 |         NA        0.33       93.07
	(...) NA
------------+-----------------------------------
      Total |     29,508      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> mining_area = Mitteldt. Revier

fut_ao_krei |
          s |      Freq.     Percent        Cum.
------------+-----------------------------------
      14729 |      4,018       19.79       19.79
      15082 |      3,531       17.39       37.17
      14713 |      1,505        7.41       44.59
      15084 |      1,504        7.41       51.99
      15088 |      1,434        7.06       59.05
      16077 |      1,207        5.94       65.00
      14730 |        822        4.05       69.05
      12066 |        707        3.48       72.53
      15091 |        689        3.39       75.92
      15002 |        523        2.58       78.50
      15087 |        431        2.12       80.62
      15089 |        318        1.57       82.18
      14612 |        317        1.56       83.74
      14625 |        287        1.41       85.16
      12071 |        270        1.33       86.49
      15085 |        245        1.21       87.69
      15001 |        159        0.78       88.48
      15003 |        112        0.55       89.03
      11000 |        110        0.54       89.57
		(...) NA
------------+-----------------------------------
      Total |     20,307      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> mining_area = Helmstedter Revier

fut_ao_krei |
          s |      Freq.     Percent        Cum.
------------+-----------------------------------
       3154 |        855       50.83       50.83
       3103 |        257       15.28       66.11
       3101 |        105        6.24       72.35
       3241 |        101        6.00       78.36
				(...) NA
------------+-----------------------------------
      Total |      1,682      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> mining_area = Rheinisches Revier

fut_ao_krei |
          s |      Freq.     Percent        Cum.
------------+-----------------------------------
       5362 |      3,805       42.00       42.00
       5315 |      1,753       19.35       61.35
       5162 |      1,466       16.18       77.53
       5913 |        502        5.54       83.07
       5334 |        349        3.85       86.92
       5113 |        236        2.60       89.53
       5358 |        200        2.21       91.73
				(...) NA
------------+-----------------------------------
      Total |      9,060      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> mining_area = Other Reviere

fut_ao_krei |
          s |      Freq.     Percent        Cum.
------------+-----------------------------------
       9376 |        542       31.81       31.81
       6435 |        177       10.39       42.19
				(...) NA
------------+-----------------------------------
      Total |      1,704      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> mining_area = .

fut_ao_krei |
          s |      Freq.     Percent        Cum.
------------+-----------------------------------
				(...) NA
------------+-----------------------------------
      Total |         NA      100.00

r; t=0.83 14:45:01

.                                                                                                                                                         
.         tab fut_wo_bula if (pretrans==3 | pretrans==7)                                                  

fut_wo_bula |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     47,973       77.05       77.05
          1 |         NA        0.02       77.06
          2 |         NA        0.01       77.08
          3 |      1,040        1.67       78.75
          4 |         NA        0.00       78.75
          5 |      4,506        7.24       85.99
          6 |        114        0.18       86.17
          7 |         NA        0.06       86.23
          8 |         NA        0.11       86.34
          9 |         NA        0.12       86.46
         10 |         NA        0.10       86.56
         11 |         NA        0.08       86.64
         12 |      2,468        3.96       90.60
         13 |         NA        0.01       90.61
         14 |      3,458        5.55       96.17
         15 |      1,988        3.19       99.36
         16 |        382        0.61       99.97
         20 |         NA        0.03      100.00
------------+-----------------------------------
      Total |     62,266      100.00
r; t=0.35 14:45:02

.         bys mining_area: tab fut_wo_bula if (pretrans==3 | pretrans==7)                 

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> mining_area = Lausitzer Revier

fut_wo_bula |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     25,778       87.36       87.36
          1 |         NA        0.01       87.37
          2 |         NA        0.01       87.37
          3 |         NA        0.04       87.41
          4 |         NA        0.00       87.42
          5 |         NA        0.07       87.48
          6 |         NA        0.04       87.53
          7 |         NA        0.02       87.55
          8 |         NA        0.11       87.66
          9 |         NA        0.12       87.77
         11 |         NA        0.09       87.86
         12 |      1,812        6.14       94.00
         13 |         NA        0.01       94.01
         14 |      1,566        5.31       99.32
         15 |        182        0.62       99.93
         16 |         NA        0.07      100.00
------------+-----------------------------------
      Total |     29,508      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> mining_area = Mitteldt. Revier

fut_wo_bula |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     15,663       77.13       77.13
          1 |         NA        0.02       77.15
          3 |         NA        0.05       77.20
          5 |         NA        0.05       77.26
          6 |         NA        0.02       77.28
          7 |         NA        0.01       77.29
          8 |         NA        0.09       77.39
          9 |         NA        0.12       77.51
         10 |         NA        0.00       77.51
         11 |         NA        0.01       77.52
         12 |        611        3.01       80.53
         13 |         NA        0.01       80.54
         14 |      1,875        9.23       89.77
         15 |      1,729        8.51       98.29
         16 |        348        1.71      100.00
------------+-----------------------------------
      Total |     20,307      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> mining_area = Helmstedter Revier

fut_wo_bula |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        605       35.97       35.97
          1 |         NA        0.18       36.15
          2 |         NA        0.18       36.33
          3 |        963       57.25       93.58
          5 |         NA        0.59       94.17
          6 |         NA        0.77       94.95
          8 |         NA        0.12       95.07
          9 |         NA        0.42       95.48
         11 |         NA        0.18       95.66
         12 |         NA        0.42       96.08
         13 |         NA        0.06       96.14
         14 |         NA        0.12       96.25
         15 |         NA        3.75      100.00
------------+-----------------------------------
      Total |      1,682      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> mining_area = Rheinisches Revier

fut_wo_bula |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      4,520       49.89       49.89
          1 |         NA        0.01       49.90
          2 |         NA        0.02       49.92
          3 |         NA        0.14       50.07
          4 |         NA        0.01       50.08
          5 |      4,443       49.04       99.12
          6 |         NA        0.04       99.16
          7 |         NA        0.32       99.48
          8 |         NA        0.11       99.59
          9 |         NA        0.07       99.66
         10 |         NA        0.04       99.70
         11 |         NA        0.04       99.75
         12 |         NA        0.03       99.78
         14 |         NA        0.03       99.81
         15 |         NA        0.04       99.86
         16 |         NA        0.03       99.89
         20 |         NA        0.11      100.00
------------+-----------------------------------
      Total |      9,060      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> mining_area = Other Reviere

fut_wo_bula |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,404       82.39       82.39
          2 |         NA        0.12       82.51
          3 |         NA        2.41       84.92
          5 |         NA        1.29       86.21
          6 |         NA        4.69       90.90
          7 |         NA        0.12       91.02
          8 |         NA        0.18       91.20
          9 |         NA        0.12       91.31
         10 |         NA        3.40       94.72
         11 |         NA        1.00       95.72
         12 |         NA        2.05       97.77
         14 |         NA        0.70       98.47
         15 |         NA        0.59       99.06
         16 |         NA        0.53       99.59
         20 |         NA        0.41      100.00
------------+-----------------------------------
      Total |      1,704      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> mining_area = .

fut_wo_bula |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |         NA       60.00       60.00
         16 |         NA       40.00      100.00
------------+-----------------------------------
      Total |         NA      100.00

r; t=0.56 14:45:02

.         bys mining_area: tab fut_wo_kreis if (pretrans==3 | pretrans==7), sort  

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> mining_area = Lausitzer Revier

fut_wo_krei |
          s |      Freq.     Percent        Cum.
------------+-----------------------------------
         -5 |     25,778       87.36       87.36
      14625 |      1,035        3.51       90.87
      12066 |        990        3.36       94.22
      12071 |        545        1.85       96.07
      14626 |        312        1.06       97.13
      12052 |        194        0.66       97.78
      14729 |        131        0.44       98.23
      15082 |        123        0.42       98.64
	  (...)			NA
------------+-----------------------------------
      Total |     29,508      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> mining_area = Mitteldt. Revier

fut_wo_krei |
          s |      Freq.     Percent        Cum.
------------+-----------------------------------
         -5 |     15,663       77.13       77.13
      14729 |      1,347        6.63       83.76
      15088 |        447        2.20       85.97
      15084 |        424        2.09       88.05
      15082 |        412        2.03       90.08
      12066 |        392        1.93       92.01
      16077 |        341        1.68       93.69
      14730 |        170        0.84       94.53
      14625 |        147        0.72       95.25
      15091 |        134        0.66       95.91
      15089 |        125        0.62       96.53
      12071 |        111        0.55       97.07
      14713 |        111        0.55       97.62
	  (...)			NA

------------+-----------------------------------
      Total |     20,307      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> mining_area = Helmstedter Revier

fut_wo_krei |
          s |      Freq.     Percent        Cum.
------------+-----------------------------------
       3154 |        855       50.83       50.83
         -5 |        605       35.97       86.80
	  (...)			NA
------------+-----------------------------------
      Total |      1,682      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> mining_area = Rheinisches Revier

fut_wo_krei |
          s |      Freq.     Percent        Cum.
------------+-----------------------------------
         -5 |      4,520       49.89       49.89
       5362 |      2,208       24.37       74.26
       5358 |        587        6.48       80.74
       5334 |        538        5.94       86.68
       5162 |        386        4.26       90.94
       5315 |        282        3.11       94.05
       5370 |        155        1.71       95.76
	  (...)			NA

------------+-----------------------------------
      Total |      9,060      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> mining_area = Other Reviere

fut_wo_krei |
          s |      Freq.     Percent        Cum.
------------+-----------------------------------
         -5 |      1,404       82.39       82.39
	  (...)			NA
------------+-----------------------------------
      Total |      1,704      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> mining_area = .

fut_wo_krei |
          s |      Freq.     Percent        Cum.
------------+-----------------------------------
         -5 |         NA       60.00       60.00
      16051 |         NA       40.00      100.00
------------+-----------------------------------
      Total |         NA      100.00

r; t=0.63 14:45:03

.                                         
.         * what fracction of people who move to new jobs remain in same kreis/bula?
.         g same_wo_kreis =. 
(1,456,038 missing values generated)
r; t=0.05 14:45:03

.         replace same_wo_kreis=0 if (pre_wo_kreis!=. & fut_wo_kreis!=.)
(62,266 real changes made)
r; t=0.05 14:45:03

.         replace same_wo_kreis=1 if (pre_wo_kreis == fut_wo_kreis) & (fut_wo_kreis!=.)
(54,594 real changes made)
r; t=0.06 14:45:03

.         tab same_wo_kreis

same_wo_kre |
         is |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      7,672       12.32       12.32
          1 |     54,594       87.68      100.00
------------+-----------------------------------
      Total |     62,266      100.00
r; t=0.17 14:45:03

.         
.         g same_wo_bula =. 
(1,456,038 missing values generated)
r; t=0.06 14:45:03

.         replace same_wo_bula=0 if (pre_wo_bula!=. & fut_wo_bula!=.)
(62,266 real changes made)
r; t=0.05 14:45:04

.         replace same_wo_bula=1 if (pre_wo_bula == fut_wo_bula) & (fut_wo_bula!=.)
(54,805 real changes made)
r; t=0.06 14:45:04

.         tab same_wo_bula

same_wo_bul |
          a |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      7,461       11.98       11.98
          1 |     54,805       88.02      100.00
------------+-----------------------------------
      Total |     62,266      100.00
r; t=0.19 14:45:04

. 
.         g same_ao_kreis =.
(1,456,038 missing values generated)
r; t=0.04 14:45:04

.         replace same_ao_kreis = 0 if pre_ao_kreis !=.
(62,266 real changes made)
r; t=0.04 14:45:04

.         replace same_ao_kreis = 1 if (pre_ao_kreis == fut_ao_kreis) & (pre_ao_kreis !=.)
(22,406 real changes made)
r; t=0.06 14:45:04

.         tab same_ao_kreis

same_ao_kre |
         is |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     39,860       64.02       64.02
          1 |     22,406       35.98      100.00
------------+-----------------------------------
      Total |     62,266      100.00
r; t=0.17 14:45:04

.                 
.         g same_ao_bula = .
(1,456,038 missing values generated)
r; t=0.04 14:45:04

.         replace same_ao_bula = 0 if pre_ao_bula != .
(62,266 real changes made)
r; t=0.04 14:45:04

.         replace same_ao_bula = 1 if (pre_ao_bula == fut_ao_bula) & (pre_ao_bula !=.)
(44,302 real changes made)
r; t=0.06 14:45:04

.         tab same_ao_bula

same_ao_bul |
          a |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     17,964       28.85       28.85
          1 |     44,302       71.15      100.00
------------+-----------------------------------
      Total |     62,266      100.00
r; t=0.21 14:45:05

.                 
.         **********************************************************************************
.         *** (2.5) Distribution of occupations in destination jobs after transitions  *****
.         **********************************************************************************
. 
.         * occupations after direct J2J move *
.         tab beruf12 if posttrans==3, sort

  top ten occupations in mining + other |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
        Maschinen-Fahrzeugtechnikberufe |      8,113       20.29       20.29
                      Other occupations |      7,841       19.61       39.89
 Führer/innenFahrzeug-,Transportgeräten |      4,706       11.77       51.66
Metallerzeugung-bearbeitung,Metallbaube |      4,246       10.62       62.28
      Unternehmensführung,-organisation |      4,123       10.31       72.59
                     Hoch-Tiefbauberufe |      4,105       10.26       82.85
TechnischeForschungs-Entwicklungsberufe |      2,346        5.87       88.72
Verkehrs-,Logistikberufe(außerFahrzeugf |      2,018        5.05       93.76
      Mechatronik-Energie-Elektroberufe |      1,134        2.84       96.60
   Gebäude-versorgungstechnische Berufe |        747        1.87       98.46
Rohstoffgewinnung-aufbereitung,Glas-Ker |        614        1.54      100.00
----------------------------------------+-----------------------------------
                                  Total |     39,993      100.00
r; t=0.27 14:45:05

.         bys decades:    tab beruf12 if (posttrans==3), sort

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> decades = 1970s-1980s

  top ten occupations in mining + other |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
        Maschinen-Fahrzeugtechnikberufe |        857       27.22       27.22
                      Other occupations |        503       15.98       43.20
 Führer/innenFahrzeug-,Transportgeräten |        422       13.41       56.61
Metallerzeugung-bearbeitung,Metallbaube |        362       11.50       68.11
Verkehrs-,Logistikberufe(außerFahrzeugf |        243        7.72       75.83
      Unternehmensführung,-organisation |        201        6.39       82.21
      Mechatronik-Energie-Elektroberufe |        162        5.15       87.36
Rohstoffgewinnung-aufbereitung,Glas-Ker |        123        3.91       91.26
   Gebäude-versorgungstechnische Berufe |        115        3.65       94.92
                     Hoch-Tiefbauberufe |         NA        2.80       97.71
TechnischeForschungs-Entwicklungsberufe |         NA        2.29      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,148      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> decades = 1990s

  top ten occupations in mining + other |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
        Maschinen-Fahrzeugtechnikberufe |      5,225       21.42       21.42
                      Other occupations |      4,140       16.97       38.40
                     Hoch-Tiefbauberufe |      3,580       14.68       53.08
 Führer/innenFahrzeug-,Transportgeräten |      3,231       13.25       66.32
Metallerzeugung-bearbeitung,Metallbaube |      2,779       11.39       77.72
      Unternehmensführung,-organisation |      1,611        6.61       84.32
TechnischeForschungs-Entwicklungsberufe |      1,477        6.06       90.38
Verkehrs-,Logistikberufe(außerFahrzeugf |      1,265        5.19       95.57
   Gebäude-versorgungstechnische Berufe |        485        1.99       97.56
      Mechatronik-Energie-Elektroberufe |        429        1.76       99.32
Rohstoffgewinnung-aufbereitung,Glas-Ker |        167        0.68      100.00
----------------------------------------+-----------------------------------
                                  Total |     24,389      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> decades = 2000s

  top ten occupations in mining + other |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                      Other occupations |      1,991       24.83       24.83
        Maschinen-Fahrzeugtechnikberufe |      1,466       18.29       43.12
      Unternehmensführung,-organisation |      1,318       16.44       59.56
Metallerzeugung-bearbeitung,Metallbaube |        817       10.19       69.75
 Führer/innenFahrzeug-,Transportgeräten |        796        9.93       79.68
TechnischeForschungs-Entwicklungsberufe |        548        6.84       86.52
                     Hoch-Tiefbauberufe |        353        4.40       90.92
Verkehrs-,Logistikberufe(außerFahrzeugf |        302        3.77       94.69
      Mechatronik-Energie-Elektroberufe |        274        3.42       98.10
   Gebäude-versorgungstechnische Berufe |         NA        1.11       99.21
Rohstoffgewinnung-aufbereitung,Glas-Ker |         NA        0.79      100.00
----------------------------------------+-----------------------------------
                                  Total |      8,017      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> decades = 2010s

  top ten occupations in mining + other |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                      Other occupations |      1,207       27.19       27.19
      Unternehmensführung,-organisation |        993       22.37       49.56
        Maschinen-Fahrzeugtechnikberufe |        565       12.73       62.29
Metallerzeugung-bearbeitung,Metallbaube |        288        6.49       68.78
      Mechatronik-Energie-Elektroberufe |        269        6.06       74.84
Rohstoffgewinnung-aufbereitung,Glas-Ker |        261        5.88       80.72
 Führer/innenFahrzeug-,Transportgeräten |        257        5.79       86.51
TechnischeForschungs-Entwicklungsberufe |        249        5.61       92.12
Verkehrs-,Logistikberufe(außerFahrzeugf |        208        4.69       96.80
                     Hoch-Tiefbauberufe |         NA        1.89       98.69
   Gebäude-versorgungstechnische Berufe |         NA        1.31      100.00
----------------------------------------+-----------------------------------
                                  Total |      4,439      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> decades = .
no observations

r; t=1.03 14:45:06

. 
.         * occupations after indirect J2J move *
.         tab beruf12 if (posttrans==3 | jposttrans==1), sort

  top ten occupations in mining + other |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                      Other occupations |     16,469       26.45       26.45
        Maschinen-Fahrzeugtechnikberufe |     10,272       16.50       42.95
                     Hoch-Tiefbauberufe |      7,264       11.67       54.61
 Führer/innenFahrzeug-,Transportgeräten |      6,509       10.45       65.07
      Unternehmensführung,-organisation |      6,013        9.66       74.72
Metallerzeugung-bearbeitung,Metallbaube |      5,997        9.63       84.35
TechnischeForschungs-Entwicklungsberufe |      3,008        4.83       89.19
Verkehrs-,Logistikberufe(außerFahrzeugf |      2,999        4.82       94.00
      Mechatronik-Energie-Elektroberufe |      1,486        2.39       96.39
   Gebäude-versorgungstechnische Berufe |      1,441        2.31       98.70
Rohstoffgewinnung-aufbereitung,Glas-Ker |        808        1.30      100.00
----------------------------------------+-----------------------------------
                                  Total |     62,266      100.00
r; t=0.38 14:45:06

.         bys decades:    tab beruf12 if (posttrans ==3 | jposttrans==1), sort

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> decades = 1970s-1980s

  top ten occupations in mining + other |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
        Maschinen-Fahrzeugtechnikberufe |        947       24.60       24.60
                      Other occupations |        705       18.31       42.91
Metallerzeugung-bearbeitung,Metallbaube |        466       12.10       55.01
 Führer/innenFahrzeug-,Transportgeräten |        465       12.08       67.09
Verkehrs-,Logistikberufe(außerFahrzeugf |        276        7.17       74.26
      Unternehmensführung,-organisation |        256        6.65       80.91
                     Hoch-Tiefbauberufe |        205        5.32       86.23
      Mechatronik-Energie-Elektroberufe |        178        4.62       90.86
Rohstoffgewinnung-aufbereitung,Glas-Ker |        145        3.77       94.62
   Gebäude-versorgungstechnische Berufe |        122        3.17       97.79
TechnischeForschungs-Entwicklungsberufe |         85        2.21      100.00
----------------------------------------+-----------------------------------
                                  Total |      3,850      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> decades = 1990s

  top ten occupations in mining + other |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                      Other occupations |      9,618       25.02       25.02
        Maschinen-Fahrzeugtechnikberufe |      6,524       16.97       42.00
                     Hoch-Tiefbauberufe |      6,044       15.72       57.72
 Führer/innenFahrzeug-,Transportgeräten |      4,241       11.03       68.76
Metallerzeugung-bearbeitung,Metallbaube |      3,872       10.07       78.83
      Unternehmensführung,-organisation |      2,583        6.72       85.55
Verkehrs-,Logistikberufe(außerFahrzeugf |      1,860        4.84       90.39
TechnischeForschungs-Entwicklungsberufe |      1,807        4.70       95.09
   Gebäude-versorgungstechnische Berufe |      1,008        2.62       97.71
      Mechatronik-Energie-Elektroberufe |        606        1.58       99.29
Rohstoffgewinnung-aufbereitung,Glas-Ker |        273        0.71      100.00
----------------------------------------+-----------------------------------
                                  Total |     38,436      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> decades = 2000s

  top ten occupations in mining + other |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                      Other occupations |      4,414       31.21       31.21
        Maschinen-Fahrzeugtechnikberufe |      2,058       14.55       45.77
      Unternehmensführung,-organisation |      2,009       14.21       59.97
 Führer/innenFahrzeug-,Transportgeräten |      1,433       10.13       70.11
Metallerzeugung-bearbeitung,Metallbaube |      1,261        8.92       79.03
                     Hoch-Tiefbauberufe |        890        6.29       85.32
TechnischeForschungs-Entwicklungsberufe |        801        5.66       90.98
Verkehrs-,Logistikberufe(außerFahrzeugf |        574        4.06       95.04
      Mechatronik-Energie-Elektroberufe |        365        2.58       97.62
   Gebäude-versorgungstechnische Berufe |        228        1.61       99.24
Rohstoffgewinnung-aufbereitung,Glas-Ker |        108        0.76      100.00
----------------------------------------+-----------------------------------
                                  Total |     14,141      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> decades = 2010s

  top ten occupations in mining + other |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
                      Other occupations |      1,732       29.66       29.66
      Unternehmensführung,-organisation |      1,165       19.95       49.61
        Maschinen-Fahrzeugtechnikberufe |        743       12.72       62.34
Metallerzeugung-bearbeitung,Metallbaube |        398        6.82       69.16
 Führer/innenFahrzeug-,Transportgeräten |        370        6.34       75.49
      Mechatronik-Energie-Elektroberufe |        337        5.77       81.26
TechnischeForschungs-Entwicklungsberufe |        315        5.39       86.66
Verkehrs-,Logistikberufe(außerFahrzeugf |        289        4.95       91.61
Rohstoffgewinnung-aufbereitung,Glas-Ker |        282        4.83       96.44
                     Hoch-Tiefbauberufe |        125        2.14       98.58
   Gebäude-versorgungstechnische Berufe |         NA        1.42      100.00
----------------------------------------+-----------------------------------
                                  Total |      5,839      100.00

--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-> decades = .
no observations

r; t=0.59 14:45:07

.         
.         * occupational transition matrix
.         g beruf12_pre=.
(1,456,038 missing values generated)
r; t=0.04 14:45:07

.         replace beruf12_pre=beruf12 if (pretrans==3 | jposttrans==1)
(62,266 real changes made)
r; t=0.05 14:45:07

.         
.         g beruf12_post=.
(1,456,038 missing values generated)
r; t=0.04 14:45:07

.         replace beruf12_post=beruf12 if (posttrans==3 | jposttrans==1)
(62,266 real changes made)
r; t=0.05 14:45:07

. 
. /* ------------------------------------------------------------------------ */
.  *      SAVE BEFORE COLLATING SPELLS -> PRECOLL.DTA
. /* ------------------------------------------------------------------------ */  
. sav ${data}\precoll.dta, replace
file \\iab.baintern.de\DFS\017\Ablagen\D01700-Projekte\D01700-COAL\data\precoll.dta saved
r; t=29.61 14:45:41

. 
. /* ------------------------------------------------------------------------ */
.  *      BASIC DESCRIPTIVE STATISTICS ON CHARACTERISTICS BEFORE COLLATING SPELLS
. /* ------------------------------------------------------------------------ */
. * NB. these are descriptive stats on spells, not on individuals
. tab ageend, m

 age at end |
   of spell |      Freq.     Percent        Cum.
------------+-----------------------------------
         18 |     22,969        1.58        1.58
         19 |     29,632        2.04        3.61
         20 |     35,408        2.43        6.04
         21 |     36,593        2.51        8.56
         22 |     31,577        2.17       10.73
         23 |     28,249        1.94       12.67
         24 |     26,305        1.81       14.47
         25 |     26,111        1.79       16.27
         26 |     26,195        1.80       18.07
         27 |     26,556        1.82       19.89
         28 |     26,634        1.83       21.72
         29 |     27,093        1.86       23.58
         30 |     27,795        1.91       25.49
         31 |     28,155        1.93       27.42
         32 |     29,260        2.01       29.43
         33 |     30,086        2.07       31.50
         34 |     30,711        2.11       33.61
         35 |     31,555        2.17       35.77
         36 |     32,198        2.21       37.99
         37 |     32,570        2.24       40.22
         38 |     33,125        2.28       42.50
         39 |     33,424        2.30       44.79
         40 |     34,119        2.34       47.14
         41 |     34,538        2.37       49.51
         42 |     34,863        2.39       51.90
         43 |     35,273        2.42       54.33
         44 |     35,217        2.42       56.74
         45 |     35,120        2.41       59.16
         46 |     34,783        2.39       61.54
         47 |     34,057        2.34       63.88
         48 |     34,316        2.36       66.24
         49 |     34,161        2.35       68.59
         50 |     34,610        2.38       70.96
         51 |     35,857        2.46       73.43
         52 |     35,560        2.44       75.87
         53 |     35,920        2.47       78.34
         54 |     37,328        2.56       80.90
         55 |     44,084        3.03       83.93
         56 |     35,760        2.46       86.38
         57 |     32,319        2.22       88.60
         58 |     30,505        2.10       90.70
         59 |     25,336        1.74       92.44
         60 |     42,466        2.92       95.35
         61 |     16,880        1.16       96.51
         62 |     12,494        0.86       97.37
         63 |     13,747        0.94       98.32
         64 |      6,197        0.43       98.74
         65 |      5,394        0.37       99.11
         66 |      3,071        0.21       99.32
         67 |      2,264        0.16       99.48
         68 |      1,740        0.12       99.60
         69 |      1,433        0.10       99.70
         70 |      1,114        0.08       99.77
         71 |        863        0.06       99.83
         72 |        722        0.05       99.88
         73 |        594        0.04       99.92
         74 |        414        0.03       99.95
         75 |        312        0.02       99.97
         76 |        406        0.03      100.00
------------+-----------------------------------
      Total |  1,456,038      100.00
r; t=0.53 14:45:42

. tab agecat2, m          

 age at end |
of spell by |
   category |      Freq.     Percent        Cum.
------------+-----------------------------------
   age18-30 |    371,117       25.49       25.49
   age31-49 |    627,531       43.10       68.59
     age50+ |    457,390       31.41      100.00
------------+-----------------------------------
      Total |  1,456,038      100.00
r; t=0.37 14:45:42

. tab frau, m

       frau |      Freq.     Percent        Cum.
------------+-----------------------------------
       mann |  1,144,856       78.63       78.63
       frau |    311,182       21.37      100.00
------------+-----------------------------------
      Total |  1,456,038      100.00
r; t=0.29 14:45:43

. tab bild, m

            Education, imputed based on |
      Fitzenberger, Osikominu & Voelter |
                                 (2008) |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
               1 no vocational training |    183,452       12.60       12.60
                  2 vocational training |  1,128,651       77.52       90.11
3 university or university of applied s |    117,696        8.08       98.20
                                      . |     26,239        1.80      100.00
----------------------------------------+-----------------------------------
                                  Total |  1,456,038      100.00
r; t=0.27 14:45:43

. tab educ2, m

        education 2 |
         categories |      Freq.     Percent        Cum.
--------------------+-----------------------------------
keine abg. Ausbild. |    183,452       12.60       12.60
    abg. Ausbildung |  1,246,347       85.60       98.20
                  . |     26,239        1.80      100.00
--------------------+-----------------------------------
              Total |  1,456,038      100.00
r; t=0.30 14:45:43

. tab mining_area, m      

       mining_area |      Freq.     Percent        Cum.
-------------------+-----------------------------------
  Lausitzer Revier |    295,563       20.30       20.30
  Mitteldt. Revier |    201,173       13.82       34.12
Helmstedter Revier |     23,330        1.60       35.72
Rheinisches Revier |    178,806       12.28       48.00
     Other Reviere |     41,946        2.88       50.88
                 . |    715,220       49.12      100.00
-------------------+-----------------------------------
             Total |  1,456,038      100.00
r; t=0.29 14:45:44

. *We want to know if we have black hole spells in the sample (statsimple=.)
. tab statsimple,m                

        0 - |
unemployed, |
 margemp or |
 ALMP / 1 - |
 employed / |
        2 - |
 vocational |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |    418,408       28.74       28.74
          1 |    729,303       50.09       78.82
          2 |     99,984        6.87       85.69
          . |    208,343       14.31      100.00
------------+-----------------------------------
      Total |  1,456,038      100.00
r; t=0.27 14:45:44

. 
. /* ------------------------------------------------------------------------ */
.  *  (4) Collate duration to estimate transition parameters 
. /* ------------------------------------------------------------------------ */                  
.         /*      Later calculated
>                 - rho (ex mu: retirement)
>                 - delta_lig
>                 - delta_nonlig
>                 - lambda_nonlig (lambda_lig we set to zero...)
>         
>                 To estimate [rho, delta_lig, delta_nonlig] we need durations of employment before transitions:
>                 Collate job spells by status for full duration of (employment) status prior to transition. 
>                         -rho (ex-mu): retirement probability (pretrans 1 and 6)
>                              rhodirect (pretrans = 1) /  rhoindirect (pretrans = 6)
>                         - delta_lig (pretrans = 7 & pretrans = 8): proba of job loss out of lignite
>                         - delta_nonlig (pretrans = 11 & pretrans = 12): proba of job loss out of non-lignite
> 
>                 To estimate [lambda_nonlig, lambda_nonlig] we need durations of unemployment after the transition.
>                 Collate spells by status for full duration of unemployment status after transition. 
>                         - lambda_nonlig (posttrans=7): finding a job in non-lignite if unemployed                               
>                         - lambda_lig (posttrans=8): finding a job in lignite if unemployed (although =0 by assumption)
>         */      
.         
.         /* ------------------------------------------------------------------------ */
.         * for rho and delta collate durations in * same status * employment before transition 
.         /* ------------------------------------------------------------------------ */
. 
.         * To estimate the duration pre-retirement and pre-unemployment, we are interested in 
.         * the whole period of an individual in which the status remains identical. 
.         * Statsimple:   0 - unemp, ALMP or margemp
.         *                               1 - normalemp
.         *                               2 - vocational training
.         
.                         * Gen a variable that counts (backwards) the number of spells with the same status ending at the transition spell
.                         sort pid begepi
r; t=0.69 14:45:45

.                         cap drop consecutive
r; t=0.00 14:45:45

.                         *Start counting at the pre-transition spell.
.                         bys pid (begepi): gen consecutive = 1 if inlist(pretrans,1,6,7,8,10,11,12) //remove 3 (j2j-transitions) because they otherwise break the consecutive series
(1,177,747 missing values generated)
r; t=0.06 14:45:45

.                         
.                         * Count as long (backwards) as status stays the same
.                         * this simply repeats the operation of adding consecutive spells until no more replacements are made.
.                         local more 1
r; t=0.00 14:45:45

.                         while `more'{
  2.                         clonevar consecutive2=consecutive
  3.                         bys pid (begepi): replace consecutive = consecutive[_n+1] + 1 if consecutive[_n+1]!=. & consecutive==. & statsimple==statsimple[_n+1] 
  4.                         count if consecutive2!=consecutive
  5.                         local more = r(N)
  6.                         drop consecutive2
  7.                         }
(1,177,747 missing values generated)
(74201 real changes made)
  74,201
(1,103,546 missing values generated)
(28621 real changes made)
  28,621
(1,074,925 missing values generated)
(10249 real changes made)
  10,249
(1,064,676 missing values generated)
(3788 real changes made)
  3,788
(1,060,888 missing values generated)
(1509 real changes made)
  1,509
(1,059,379 missing values generated)
(599 real changes made)
  599
(1,058,780 missing values generated)
(281 real changes made)
  281
(1,058,499 missing values generated)
(133 real changes made)
  133
(1,058,366 missing values generated)
(82 real changes made)
  NA
(1,058,284 missing values generated)
(49 real changes made)
  NA
(1,058,235 missing values generated)
(31 real changes made)
  NA
(1,058,204 missing values generated)
(20 real changes made)
  NA
(1,058,184 missing values generated)
(14 real changes made)
  NA
(1,058,170 missing values generated)
(12 real changes made)
  NA
(1,058,158 missing values generated)
(9 real changes made)
  NA
  (1,058,149 missing values generated)
(6 real changes made)
  NA
(1,058,143 missing values generated)
(5 real changes made)
  NA
(1,058,138 missing values generated)
(3 real changes made)
  NA
(1,058,135 missing values generated)
(3 real changes made)
  NA
(1,058,132 missing values generated)
(2 real changes made)
  NA
(1,058,130 missing values generated)
(2 real changes made)
  NA
(1,058,128 missing values generated)
(2 real changes made)
  NA
(1,058,126 missing values generated)
(2 real changes made)
  NA
(1,058,124 missing values generated)
(2 real changes made)
  NA
(1,058,122 missing values generated)
(2 real changes made)
  NA
(1,058,120 missing values generated)
(2 real changes made)
  NA
(1,058,118 missing values generated)
(1 real change made)
  NA
(1,058,117 missing values generated)
(1 real change made)
  NA
(1,058,116 missing values generated)
(1 real change made)
  NA
(1,058,115 missing values generated)
(0 real changes made)
  NA
r; t=6.97 14:45:52

.         
.                         * Use the maximum number of spells with the same status as the end for the loop below
.                         tab consecutive

consecutive |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |    278,291       69.94       69.94
          2 |     74,201       18.65       88.58
          3 |     28,621        7.19       95.78
          4 |     10,249        2.58       98.35
          5 |      3,788        0.95       99.30
          6 |      1,509        0.38       99.68
          7 |        599        0.15       99.83
          8 |        281        0.07       99.90
          9 |        133        0.03       99.94
         10 |         NA        0.02       99.96
         11 |         NA        0.01       99.97
         12 |         NA        0.01       99.98
         13 |         NA        0.01       99.98
         14 |         NA        0.00       99.99
         15 |         NA        0.00       99.99
         16 |         NA        0.00       99.99
         17 |         NA        0.00       99.99
         18 |         NA        0.00       99.99
         19 |         NA        0.00       99.99
         20 |         NA        0.00      100.00
         21 |         NA        0.00      100.00
         22 |         NA        0.00      100.00
         23 |         NA        0.00      100.00
         24 |         NA        0.00      100.00
         25 |         NA        0.00      100.00
         26 |         NA        0.00      100.00
         27 |         NA        0.00      100.00
         28 |         NA        0.00      100.00
         29 |         NA        0.00      100.00
         30 |         NA        0.00      100.00
------------+-----------------------------------
      Total |    397,923      100.00
r; t=0.21 14:45:52

.                         sum consecutive

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
 consecutive |    397,923    1.488798    .9614046          1         30
r; t=0.06 14:45:52

.                         local end = r(max)
r; t=0.00 14:45:52

.                         disp(`end')
30
r; t=0.00 14:45:52

. 
.                         forvalues i = 2/`end' {
  2.                                 * Manipulate begepi
.                                 // Here we extend the first spell within each stack of consecutive spells
.                                 bys pid (begepi): replace begepi = begepi[_n-1] if consecutive[_n-1] == `i' & consecutive == 1 
  3.                                 * Drop the *first of consecutive spells*
.                                 bys pid (begepi): drop if consecutive == `i' & consecutive[_n+1] == 1
  4.                                 }
(74,191 real changes made)
(74,201 observations deleted)
(28,609 real changes made)
(28,621 observations deleted)
(10,239 real changes made)
(10,249 observations deleted)
(3,786 real changes made)
(3,788 observations deleted)
(1,508 real changes made)
(1,509 observations deleted)
(599 real changes made)
(599 observations deleted)
(281 real changes made)
(281 observations deleted)
(133 real changes made)
(133 observations deleted)
(NA real changes made)
(NA observations deleted)
(NA real changes made)
(NA observations deleted)
(NA real changes made)
(NA observations deleted)
(NA real changes made)
(NA observations deleted)
(NA real changes made)
(NA observations deleted)
(NA real changes made)
(NA observations deleted)
(NA real changes made)
(NA observations deleted)
(NA real changes made)
(NA observations deleted)
(NA real changes made)
(NA observations deleted)
(NA real changes made)
(NA observations deleted)
(NA real changes made)
(NA observations deleted)
(NA real changes made)
(NA observations deleted)
(NA real changes made)
(NA observations deleted)
(NA real changes made)
(NA observations deleted)
(NA real changes made)
(NA observations deleted)
(NA real changes made)
(NA observations deleted)
(NA real changes made)
(NA observations deleted)
(NA real changes made)
(NA observations deleted)
(NA real change made)
(NA observation deleted)
(NA real change made)
(NA observation deleted)
(NA real change made)
(NA observation deleted)
r; t=49.18 14:46:41

. 
.                         * REMARK: Only the information of the *last spell* is kept! 
.                         * We do not want to use this for descriptive statistics on income etc. 
.                         * (thus will go back to dataset prior to collating status (dataset called precoll) below)
. 
.         /* ------------------------------------------------------------- */
.         * for lambda Collate durations in unemp after transition 
.         /* ------------------------------------------------------------- */
.                         
.                         sort pid begepi
r; t=0.17 14:46:41

.                         cap drop consecutive
r; t=0.00 14:46:41

.                         *Start counting at relevant posttransition spells.
.                         bys pid (begepi): gen consecutive = 1 if inlist(posttrans,7,8,11,12) 
(1,134,002 missing values generated)
r; t=0.06 14:46:41

. 
.                         * Count as long (backwards) as status stays the same
.                         * this simply repeats the operation of adding consecutive spells until no more replacements are made.
.                         local more 1
r; t=0.00 14:46:41

.                         while `more'{
  2.                         clonevar consecutive2=consecutive
  3.                         bys pid (begepi): replace consecutive = consecutive[_n-1] + 1 if consecutive[_n-1]!=. & consecutive==. & status==status[_n-1] 
  4.                         count if consecutive2!=consecutive
  5.                         local more = r(N)
  6.                         drop consecutive2
  7.                         }       
(1,134,002 missing values generated)
(0 real changes made)
  0
r; t=0.18 14:46:42

.                         
.                                         
.                         * Generate duration of status
.                         cap drop dur
r; t=0.14 14:46:42

.                         gen dur = (endepi - begepi) + 1
r; t=0.07 14:46:42

.                         label var dur "duration in labour mkt status"
r; t=0.01 14:46:42

.         
.                         
. /* ------------------------------------------------------------------------ */
.  *      SAVE AFTER COLLATING SPELLS -> POSTCOLL.DTA
. /* ------------------------------------------------------------------------ */                          
. *save ${data}\delme4.dta, replace
. save ${data}\postcoll.dta, replace
file \\iab.baintern.de\DFS\017\Ablagen\D01700-Projekte\D01700-COAL\data\postcoll.dta saved
r; t=25.41 14:47:07

. 
. /* ------------------------------------------------------------------------ */
.  *      BASIC DESCRIPTIVE STATISTICS ON CHARACTERISTICS AFTER COLLATING SPELLS
. /* ------------------------------------------------------------------------ */
. tab ageend, m

 age at end |
   of spell |      Freq.     Percent        Cum.
------------+-----------------------------------
         18 |     21,768        1.63        1.63
         19 |     28,300        2.12        3.75
         20 |     34,000        2.54        6.29
         21 |     34,885        2.61        8.90
         22 |     29,609        2.22       11.12
         23 |     26,142        1.96       13.07
         24 |     24,023        1.80       14.87
         25 |     23,683        1.77       16.64
         26 |     23,637        1.77       18.41
         27 |     23,755        1.78       20.19
         28 |     23,571        1.76       21.95
         29 |     23,973        1.79       23.75
         30 |     24,492        1.83       25.58
         31 |     24,865        1.86       27.44
         32 |     25,834        1.93       29.37
         33 |     26,602        1.99       31.36
         34 |     27,111        2.03       33.39
         35 |     27,915        2.09       35.48
         36 |     28,567        2.14       37.62
         37 |     28,852        2.16       39.78
         38 |     29,324        2.19       41.97
         39 |     29,680        2.22       44.19
         40 |     30,305        2.27       46.46
         41 |     30,852        2.31       48.77
         42 |     31,093        2.33       51.10
         43 |     31,537        2.36       53.45
         44 |     31,871        2.38       55.84
         45 |     31,985        2.39       58.23
         46 |     31,718        2.37       60.61
         47 |     31,124        2.33       62.94
         48 |     31,467        2.35       65.29
         49 |     31,236        2.34       67.63
         50 |     31,586        2.36       69.99
         51 |     32,890        2.46       72.45
         52 |     32,476        2.43       74.88
         53 |     32,727        2.45       77.33
         54 |     33,702        2.52       79.85
         55 |     39,446        2.95       82.80
         56 |     33,986        2.54       85.35
         57 |     31,364        2.35       87.69
         58 |     29,829        2.23       89.93
         59 |     24,953        1.87       91.79
         60 |     42,286        3.16       94.96
         61 |     16,760        1.25       96.21
         62 |     12,404        0.93       97.14
         63 |     13,716        1.03       98.17
         64 |      6,183        0.46       98.63
         65 |      5,392        0.40       99.03
         66 |      3,068        0.23       99.26
         67 |      2,264        0.17       99.43
         68 |      1,740        0.13       99.56
         69 |      1,433        0.11       99.67
         70 |      1,114        0.08       99.75
         71 |        863        0.06       99.82
         72 |        722        0.05       99.87
         73 |        594        0.04       99.92
         74 |        414        0.03       99.95
         75 |        312        0.02       99.97
         76 |        406        0.03      100.00
------------+-----------------------------------
      Total |  1,336,406      100.00
r; t=0.43 14:47:08

. tab agecat2, m          

 age at end |
of spell by |
   category |      Freq.     Percent        Cum.
------------+-----------------------------------
   age18-30 |    341,838       25.58       25.58
   age31-49 |    561,938       42.05       67.63
     age50+ |    432,630       32.37      100.00
------------+-----------------------------------
      Total |  1,336,406      100.00
r; t=0.25 14:47:08

. tab frau, m

       frau |      Freq.     Percent        Cum.
------------+-----------------------------------
       mann |  1,044,343       78.15       78.15
       frau |    292,063       21.85      100.00
------------+-----------------------------------
      Total |  1,336,406      100.00
r; t=0.23 14:47:08

. tab educ2, m

        education 2 |
         categories |      Freq.     Percent        Cum.
--------------------+-----------------------------------
keine abg. Ausbild. |    169,469       12.68       12.68
    abg. Ausbildung |  1,142,849       85.52       98.20
                  . |     24,088        1.80      100.00
--------------------+-----------------------------------
              Total |  1,336,406      100.00
r; t=0.24 14:47:08

. tab mining_area, m

       mining_area |      Freq.     Percent        Cum.
-------------------+-----------------------------------
  Lausitzer Revier |    247,364       18.51       18.51
  Mitteldt. Revier |    173,031       12.95       31.46
Helmstedter Revier |     21,896        1.64       33.10
Rheinisches Revier |    154,643       11.57       44.67
     Other Reviere |     38,673        2.89       47.56
                 . |    700,799       52.44      100.00
-------------------+-----------------------------------
             Total |  1,336,406      100.00
r; t=0.26 14:47:09

. *We want to know if we have black hole spells in the sample (statsimple=.)
. tab statsimple,m        

        0 - |
unemployed, |
 margemp or |
 ALMP / 1 - |
 employed / |
        2 - |
 vocational |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |    418,408       31.31       31.31
          1 |    612,092       45.80       77.11
          2 |     97,563        7.30       84.41
          . |    208,343       15.59      100.00
------------+-----------------------------------
      Total |  1,336,406      100.00
r; t=0.29 14:47:09

. 
.                 *************************************************************** 
.                 *****  Number of distinct persons working in coal in 2017 ****          
.                 *************************************************************** 
.                 count if begepi<mdy(6,1,2017) & endepi>mdy(6,1,2017) & thisspelllignite==1 & statsimple==1
  12,286
r; t=0.11 14:47:09

.                 putexcel set results/${samplefolder}/5_sample${sample}_wholesample_stats, sheet("nb_distinctpersons_coal2017") modify
r; t=0.16 14:47:09

.                 putexcel B1=("nb of distinct persons working in coal2017") A2= ("whole sample") B2=(r(N))
file results/two/5_sample2_wholesample_stats.xlsx saved
r; t=0.13 14:47:09

.                 `putexcelclose' 
r; t=0.00 14:47:09

. 
.                 *************************************************************** 
.                 ********  Age distribution in lignite in 2017 ***************                   
.                 *************************************************************** 
.                 * Population of lignite workers in latest period *
.                 cap drop presentcoal2017 agecoal2017
r; t=0.00 14:47:09

.                 gen presentcoal2017=0
r; t=0.03 14:47:09

.                 replace presentcoal2017=1 if thisspelllignite==1 & begepi<mdy(6,1,2017) & endepi>mdy(6,1,2017) 
(12,863 real changes made)
r; t=0.08 14:47:09

.                 gen agecoal2017=2017-year(geb_dat) if presentcoal2017==1 
(1,323,543 missing values generated)
r; t=0.03 14:47:09

.                 tab agecoal2017, matrow(matname)

agecoal2017 |      Freq.     Percent        Cum.
------------+-----------------------------------
         18 |        105        0.82        0.82
         19 |        120        0.93        1.75
         20 |        138        1.07        2.82
         21 |        165        1.28        4.10
         22 |        147        1.14        5.25
         23 |        154        1.20        6.44
         24 |        170        1.32        7.77
         25 |        151        1.17        8.94
         26 |        172        1.34       10.28
         27 |        279        2.17       12.45
         28 |        243        1.89       14.34
         29 |        219        1.70       16.04
         30 |        227        1.76       17.80
         31 |        241        1.87       19.68
         32 |        231        1.80       21.47
         33 |        202        1.57       23.04
         34 |        195        1.52       24.56
         35 |        172        1.34       25.90
         36 |        151        1.17       27.07
         37 |        130        1.01       28.08
         38 |         NA        0.63       28.71
         39 |         NA        0.59       29.30
         40 |         NA        0.66       29.96
         41 |         NA        0.68       30.64
         42 |         NA        0.65       31.28
         43 |         NA        0.63       31.91
         44 |         NA        0.65       32.57
         45 |        110        0.86       33.42
         46 |        197        1.53       34.95
         47 |        235        1.83       36.78
         48 |        236        1.83       38.61
         49 |        289        2.25       40.86
         50 |        344        2.67       43.54
         51 |        490        3.81       47.35
         52 |        512        3.98       51.33
         53 |        589        4.58       55.90
         54 |        636        4.94       60.85
         55 |        687        5.34       66.19
         56 |        749        5.82       72.01
         57 |        580        4.51       76.52
         58 |        597        4.64       81.16
         59 |        553        4.30       85.46
         60 |        536        4.17       89.63
         61 |        498        3.87       93.50
         62 |        455        3.54       97.04
         63 |        301        2.34       99.38
         64 |         NA        0.41       99.79
         65 |         NA        0.18       99.97
         66 |         NA        0.01       99.98
         68 |         NA        0.02       99.99
         76 |         NA        0.01      100.00
------------+-----------------------------------
      Total |     12,863      100.00
r; t=0.19 14:47:10

.                 capture noisily estpost tab agecoal2017

 agecoal2017 |      e(b)     e(pct)  e(cumpct) 
-------------+---------------------------------
          18 |       105   .8162948   .8162948 
          19 |       120   .9329083   1.749203 
          20 |       138   1.072845   2.822048 
          21 |       165   1.282749   4.104797 
          22 |       147   1.142813   5.247609 
          23 |       154   1.197232   6.444842 
          24 |       170    1.32162   7.766462 
          25 |       151    1.17391   8.940372 
          26 |       172   1.337169   10.27754 
          27 |       279   2.169012   12.44655 
          28 |       243   1.889139   14.33569 
          29 |       219   1.702558   16.03825 
          30 |       227   1.764752     17.803 
          31 |       241   1.873591   19.67659 
          32 |       231   1.795849   21.47244 
          33 |       202   1.570396   23.04284 
          34 |       195   1.515976   24.55881 
          35 |       172   1.337169   25.89598 
          36 |       151    1.17391   27.06989 
          37 |       130   1.010651   28.08054 
          38 |        NA   .6297131   28.71025 
          39 |        NA   .5908419    29.3011 
          40 |        NA   .6608101   29.96191 
          41 |        NA   .6763585   30.63826 
          42 |        NA   .6452616   31.28353 
          43 |        NA   .6297131   31.91324 
          44 |        NA   .6530358   32.56628 
          45 |       110    .855166   33.42144 
          46 |       197   1.531525   34.95297 
          47 |       235   1.826946   36.77991 
          48 |       236    1.83472   38.61463 
          49 |       289   2.246754   40.86139 
          50 |       344   2.674337   43.53572 
          51 |       490   3.809376    47.3451 
          52 |       512   3.980409   51.32551 
          53 |       589   4.579025   55.90453 
          54 |       636   4.944414   60.84895 
          55 |       687     5.3409   66.18985 
          56 |       749   5.822903   72.01275 
          57 |       580   4.509057   76.52181 
          58 |       597   4.641219   81.16303 
          59 |       553   4.299153   85.46218 
          60 |       536   4.166991   89.62917 
          61 |       498    3.87157   93.50074 
          62 |       455   3.537277   97.03802 
          63 |       301   2.340045   99.37806 
          64 |        NA   .4120345    99.7901 
          65 |        NA   .1788074    99.9689 
          66 |        NA   .0077742   99.97668 
          68 |        NA   .0155485   99.99223 
          76 |        NA   .0077742        100 
-------------+---------------------------------
       Total |     12863        100            
r; t=0.38 14:47:10

.                 if _rc!=2000{
.                         putexcel set results/${samplefolder}/5_sample${sample}_wholesample_stats, sheet("distrib_age_coal2017") modify
r; t=0.02 14:47:10
.                         putexcel B1=("age in coal in 2017") A2= matrix(matname) B2=matrix(e(b)')
file results/two/5_sample2_wholesample_stats.xlsx saved
r; t=0.08 14:47:10
.                         `putexcelclose'
r; t=0.01 14:47:10
.                 }
r; t=0.12 14:47:10

.                 
.                 *************************************************************** 
.                 ********(4.0.a ) Diagnostics on spell durations ***************                 
.                 *************************************************************** 
. 
.         * JAERE - R2.MC7 - Distribution of length of spells in lignite
.         * in 7biocoal
.                                 
.                 * (a) transition from employment to retirement or unemployment
.                         * Distribution of duration of last pre-transition status
.                         
.                         sum dur if inlist(pretrans,1,6,7,8,11,12), detail

                duration in labour mkt status
-------------------------------------------------------------
      Percentiles      Smallest
 1%            2              1
 5%           17              1
10%           40              1       Obs             226,556
25%          133              1       Sum of wgt.     226,556

50%          344                      Mean           898.6263
                        Largest       Std. dev.      1477.641
75%         1022          15492
90%         2307          15494       Variance        2183423
95%         3743          15553       Skewness       3.467497
99%         7737          15584       Kurtosis       18.76228
r; t=0.85 14:47:11

.                 
.                         * Satus before transition 
.                         tab pretrans statsimple

                      | 0 - unemployed, margemp or ALMP
                      | / 1 - employed / 2 - vocational
             pretrans |         0          1          2 |     Total
----------------------+---------------------------------+----------
Not pre (observed) tr |   414,921    315,090     95,420 |   825,431 
pre retirement (witho |        NA      9,366          0 |     9,443 
pre trans'n 2 vocatio |         0        159          0 |       159 
pre trans'n 2 other n |         0     24,182          0 |    24,182 
pre trans'n to unem/A |         0     14,709          0 |    14,709 
pre trans'n 2 unemp/A |         0     22,273          0 |    22,273 
pre trans'n 2 unemp/A |         0      3,873          0 |     3,873 
pre trans'n 2 black h |     3,410     46,182      2,143 |    51,735 
pre transition out of |         0    171,765          0 |   171,765 
pre transition out of |         0      4,493          0 |     4,493 
----------------------+---------------------------------+----------
                Total |   418,408    612,092     97,563 | 1,128,063 
r; t=0.32 14:47:11

. 
.                         * Duration in status before transition
.                         foreach i in 0 1 2 3 6 7 8 10 11 12 {
  2.                                 local j = `i'+2
  3.                                 di "Duration in status before pretrans `i'"     
  4.                                 capture noisily estpost sum dur if pretrans == `i', d 
  5.                                 if _rc!=2000{
  6.                                         putexcel set results/${samplefolder}/5_sample${sample}_wholesample_stats, sheet("duration_pretrans") modify
  7.                                         putexcel A`j'=("pretrans_`i'") B1=("nb spells") B`j'=matrix(e(count)) C1=("mean") C`j'=matrix(e(mean))  
  8.                                         `putexcelclose'
  9.                                 }
 10.                         }
Duration in status before pretrans 0

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
         dur |   1033774    1033774   682.3441    1523251   1234.201    4.05137   24.96681   7.05e+08          1      15706          4         22         33         88        260        681 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
         dur |      1767       2983       6362 
file results/two/5_sample2_wholesample_stats.xlsx saved
Duration in status before pretrans 1

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
         dur |      9443       9443   3725.876    9079266   3013.182   .8907407   3.985963   3.52e+07          1      15584          9         33        119       1065       3561       5569 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
         dur |      8005       8918      14152 
file results/two/5_sample2_wholesample_stats.xlsx saved
Duration in status before pretrans 2

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
         dur |       159        159   732.0314   595310.8   771.5639   1.775125   6.148202     116393          8       3773         13         40         74        212        397       1096 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
         dur |      1719       2647       3530 
file results/two/5_sample2_wholesample_stats.xlsx saved
Duration in status before pretrans 3

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
         dur |     24182      24182   1374.507    3576353   1891.125   3.040183   13.96014   3.32e+07          1      14245         31         92        196        414        714       1369 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
         dur |      3776       5280       9557 
file results/two/5_sample2_wholesample_stats.xlsx saved
Duration in status before pretrans 6

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
         dur |     14709      14709   2450.923    5922591   2433.637   1.609893   4.857801   3.61e+07          1      14610         45        213        366        823       1461       2647 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
         dur |      6940       8036       9678 
file results/two/5_sample2_wholesample_stats.xlsx saved
Duration in status before pretrans 7

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
         dur |     22273      22273   893.5601   999788.7   999.8943   3.836856   28.62678   1.99e+07          1      15432         29        151        213        305        547       1265 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
         dur |      1827       2557       4829 
file results/two/5_sample2_wholesample_stats.xlsx saved
Duration in status before pretrans 8

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
         dur |      3873       3873    771.897    1988467    1410.13   4.664029    31.2136    2989557          1      15251          3         31         79        184        352        564 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
         dur |      1827       3095       7671 
file results/two/5_sample2_wholesample_stats.xlsx saved
Duration in status before pretrans 10

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
         dur |     51735      51735   2256.439    7392659   2718.945   1.775249   6.232411   1.17e+08          1      15609          9         49        105        335       1096       3317 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
         dur |      6210       7755      12406 
file results/two/5_sample2_wholesample_stats.xlsx saved
Duration in status before pretrans 11

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
         dur |    171765     171765   619.6698   947249.9   973.2676   3.507515   20.94752   1.06e+08          1      14338          2         13         31         98        266        672 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
         dur |      1686       2557       4749 
file results/two/5_sample2_wholesample_stats.xlsx saved
Duration in status before pretrans 12

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
         dur |      4493       4493   673.4293    1019240   1009.574   3.635945   24.73353    3025718          1      12744          4         21         43        121        306        731 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
         dur |      1828       2588       4718 
file results/two/5_sample2_wholesample_stats.xlsx saved
r; t=15.56 14:47:27

.                         
.                 * (b) transition from unemployment to employment
.                         * Satus before transition 
.                         tab posttrans statsimple

                      | 0 - unemployed, margemp or ALMP
                      | / 1 - employed / 2 - vocational
            posttrans |         0          1          2 |     Total
----------------------+---------------------------------+----------
Not post trans'n out  |   201,066    581,008     97,403 |   879,477 
in retirement (no min |       229         NA         NA |       317 
 post-lignite vocatio |         0          0        158 |       158 
post-lignite normal e |         0     30,998          0 |    30,998 
post-lignite unemp/AL |    14,709          0          0 |    14,709 
post-lignite unemp/AL |    22,273          0          0 |    22,273 
post-lignite unemp/AL |     3,873          0          0 |     3,873 
transition out of NON |   171,765          0          0 |   171,765 
transition out of NON |     4,493          0          0 |     4,493 
----------------------+---------------------------------+----------
                Total |   418,408    612,092     97,563 | 1,128,063 
r; t=0.34 14:47:27

.                         
.                         * Duration in status before transition
.                         foreach i in 0 1 2 3 6 7 8 10 11 12 {
  2.                                 local j = `i'+2
  3.                                 di "Duration in status before posttrans `i'"    
  4.                                 capture noisily estpost sum dur if posttrans == `i', d 
  5.                                 if _rc!=2000{
  6.                                         putexcel set results/${samplefolder}/5_sample${sample}_wholesample_stats, sheet("duration_posttrans") modify
  7.                                         putexcel A`j'=("posttrans_`i'") B1=("nb spells") B`j'=matrix(e(count)) C1=("mean") C`j'=matrix(e(mean))  
  8.                                         `putexcelclose'
  9.                                 }       
 10.                         }
Duration in status before posttrans 0

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
         dur |   1036085    1036085   875.5846    2315266     1521.6   3.563231   19.25576   9.07e+08          1      15706          4         26         39        110        345        880 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
         dur |      2345       3957       7670 
file results/two/5_sample2_wholesample_stats.xlsx saved
Duration in status before posttrans 1

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
         dur |       317        317   662.6435   768915.2   876.8781   3.212981   16.28783     210058          1       6559          1         25         61        122        396        792 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
         dur |      1341       2223       4566 
file results/two/5_sample2_wholesample_stats.xlsx saved
Duration in status before posttrans 2

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
         dur |       158        158   153.0633   11064.82   105.1895   .7739381   2.513485      24184          1        366          1         16         31         91        122        184 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
         dur |       335        365        365 
file results/two/5_sample2_wholesample_stats.xlsx saved
Duration in status before posttrans 3

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
         dur |     30998      30998   1610.753    3195540   1787.607   2.283228     9.6975   4.99e+07          1      14610         31        102        198        457        986       2180 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
         dur |      3773       5286       8647 
file results/two/5_sample2_wholesample_stats.xlsx saved
Duration in status before posttrans 6

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
         dur |     14709      14709   1350.589   571951.4   756.2747   2.100848   15.42472   1.99e+07          2       8828        116        364        365        914       1369       1765 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
         dur |      2007       2367       3999 
file results/two/5_sample2_wholesample_stats.xlsx saved
Duration in status before posttrans 7

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
         dur |     22273      22273   556.7482   404051.2   635.6502   3.055947   18.61346   1.24e+07          1       7397          3         22         45        134        365        776 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
         dur |      1219       1647       3044 
file results/two/5_sample2_wholesample_stats.xlsx saved
Duration in status before posttrans 8

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
         dur |      3873       3873   181.4743   60441.04   245.8476   3.863434   29.92208     702850          1       3957          1          3          6         35         98        227 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
         dur |       396        664       1247 
file results/two/5_sample2_wholesample_stats.xlsx saved
Duration in status before posttrans 10

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
         dur |     51735      51735    338.089   479555.9   692.4998    6.40217   63.62786   1.75e+07         28      14640         30         35         44         65        125        364 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
         dur |       671       1461       3555 
file results/two/5_sample2_wholesample_stats.xlsx saved
Duration in status before posttrans 11

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
         dur |    171765     171765   291.9728   239997.2   489.8951   4.420008   31.41372   5.02e+07          1       7716          2          9         18         51        123        311 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
         dur |       731       1150       2490 
file results/two/5_sample2_wholesample_stats.xlsx saved
Duration in status before posttrans 12

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
         dur |      4493       4493   248.1765   106191.7   325.8707   4.035477   29.74029    1115057          1       4748          3         15         30         64        150        304 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
         dur |       565        822       1683 
file results/two/5_sample2_wholesample_stats.xlsx saved
r; t=13.64 14:47:41

. 
.                         * detailed by sex/year
.                         tabulate frau if (posttrans==7 | posttrans==8), summarize(dur) 

            |  Summary of duration in labour mkt
            |               status
       frau |        Mean   Std. dev.       Freq.
------------+------------------------------------
       mann |   385.59393   471.70564      18,120
       frau |   762.06591    779.6285       8,026
------------+------------------------------------
      Total |   501.15899   609.03183      26,146
r; t=0.95 14:47:42

.                         tabulate jahrend if (posttrans==7 | posttrans==8), summarize(dur) 

            |  Summary of duration in labour mkt
year at end |               status
   of spell |        Mean   Std. dev.       Freq.
------------+------------------------------------
       1976 |   246.70588   142.36606          17
       1977 |   143.40541   138.93033          37
       1978 |   118.51429   103.23741          35
       1979 |   159.73171   245.53635          41
       1980 |   109.68889   153.20809          45
       1981 |   131.67797   142.05189          59
       1982 |   174.06349   239.34934         126
       1983 |   211.32955   146.83244         264
       1984 |   393.92623   272.98101         122
       1985 |   399.39535    456.1661          86
       1986 |   323.78333    455.4691          60
       1987 |   343.50943   468.66076          53
       1988 |   190.63158   199.45754          57
       1989 |   266.58824   496.73156          51
       1990 |      335.08   441.06235          50
       1991 |   188.81818   202.43635          44
       1992 |   67.859604   80.720602       1,161
       1993 |   168.70311   125.20161       4,665
       1994 |   425.94611   209.90691       3,173
       1995 |   689.34474   316.68276       2,280
       1996 |   573.77426   485.53652       2,494
       1997 |   567.96861    470.7674       2,166
       1998 |   677.16567   565.20548       1,998
       1999 |   654.47558   593.87291       1,556
       2000 |   651.83065   710.92464       1,305
       2001 |   679.76399   740.41329         822
       2002 |   782.96917   798.53536         519
       2003 |   639.09037   852.52805         509
       2004 |   889.76886   1051.2857         411
       2005 |   755.85879    1021.369         347
       2006 |   804.00338   1119.8606         296
       2007 |     673.724   1064.3302         250
       2008 |   883.80702   1375.5936         228
       2009 |   1058.5944   1673.0165         143
       2010 |   1208.7885    1921.968         156
       2011 |   857.84615    1575.677         104
       2012 |   368.82143   1038.6873          84
       2013 |   368.40278   766.11492          72
       2014 |   461.63768   1059.8275          69
       2015 |   947.67188   1883.8501          64
       2016 |   358.15873   937.97954          63
       2017 |   356.82813   704.54294          64
------------+------------------------------------
      Total |   501.15899   609.03183      26,146
r; t=1.08 14:47:43

.                         tabulate jahrend frau if (posttrans==7 | posttrans==8), summarize(dur) 

                Means, Standard Deviations and Frequencies
                     of duration in labour mkt status

   year at |
    end of |        frau
     spell |      mann       frau |     Total
-----------+----------------------+----------
      1976 | 247.06667        244 | 246.70588
           | 145.03816  172.53405 | 142.36606
           |        NA         NA |        NA
-----------+----------------------+----------
      1977 | 160.03571  91.666667 | 143.40541
           | 154.51561  47.953102 | 138.93033
           |        NA         NA |        NA
-----------+----------------------+----------
      1978 |       169  88.681818 | 118.51429
           | 157.92614   22.22051 | 103.23741
           |        NA         NA |        NA
-----------+----------------------+----------
      1979 | 206.82143  58.307692 | 159.73171
           | 279.51679  94.077437 | 245.53635
           |        NA         NA |        NA
-----------+----------------------+----------
      1980 | 124.32353  64.454545 | 109.68889
           | 167.96928   84.82849 | 153.20809
           |        NA         NA |        NA
-----------+----------------------+----------
      1981 | 138.46809  105.08333 | 131.67797
           | 138.34218  159.35635 | 142.05189
           |        NA         NA |        NA
-----------+----------------------+----------
      1982 | 187.48148  93.555556 | 174.06349
           | 254.73756  69.256745 | 239.34934
           |       108         NA |       126
-----------+----------------------+----------
      1983 | 216.95082     142.75 | 211.32955
           |  147.0229   128.9271 | 146.83244
           |       244         NA |       264
-----------+----------------------+----------
      1984 | 427.54545      85.75 | 393.92623
           | 265.24832  87.322938 | 272.98101
           |       110         NA |       122
-----------+----------------------+----------
      1985 | 504.25397  112.17391 | 399.39535
           | 476.95459  212.47559 |  456.1661
           |        NA         NA |        NA
-----------+----------------------+----------
      1986 | 444.26316  115.68182 | 323.78333
           | 532.39199   108.6775 |  455.4691
           |        NA         NA |        NA
-----------+----------------------+----------
      1987 | 442.06061      180.9 | 343.50943
           | 559.03376  173.43098 | 468.66076
           |        NA         NA |        NA
-----------+----------------------+----------
      1988 | 251.65714  93.545455 | 190.63158
           | 232.29367  51.410326 | 199.45754
           |        NA         NA |        NA
-----------+----------------------+----------
      1989 |  350.3125  125.57895 | 266.58824
           | 609.48939  110.35665 | 496.73156
           |        NA         NA |        NA
-----------+----------------------+----------
      1990 | 487.42308  170.04167 |    335.08
           | 560.29558  136.23237 | 441.06235
           |        NA         NA |        NA
-----------+----------------------+----------
      1991 |    242.12  118.68421 | 188.81818
           | 244.82397  94.028986 | 202.43635
           |        NA         NA |        NA
-----------+----------------------+----------
      1992 | 62.972445  82.537931 | 67.859604
           | 84.825943  64.840184 | 80.720602
           |       871        290 |      1161
-----------+----------------------+----------
      1993 | 144.62947  238.45029 | 168.70311
           | 111.80996  135.42583 | 125.20161
           |      3468       1197 |      4665
-----------+----------------------+----------
      1994 |   371.806  510.34355 | 425.94611
           | 202.47547  192.80433 | 209.90691
           |      1933       1240 |      3173
-----------+----------------------+----------
      1995 | 609.86273  786.31743 | 689.34474
           | 326.85067  274.35932 | 316.68276
           |      1253       1027 |      2280
-----------+----------------------+----------
      1996 | 403.34934  916.68237 | 573.77426
           | 416.54539  430.22198 | 485.53652
           |      1666        828 |      2494
-----------+----------------------+----------
      1997 | 438.72757  876.81221 | 567.96861
           | 345.84267  574.85793 |  470.7674
           |      1527        639 |      2166
-----------+----------------------+----------
      1998 | 511.15948  1058.4868 | 677.16567
           | 414.11503   671.6638 | 565.20548
           |      1392        606 |      1998
-----------+----------------------+----------
      1999 |  505.3589  1008.6681 | 654.47558
           | 450.32896  728.88316 | 593.87291
           |      1095        461 |      1556
-----------+----------------------+----------
      2000 | 475.09549  1093.4316 | 651.83065
           | 540.93034   874.9704 | 710.92464
           |       932        373 |      1305
-----------+----------------------+----------
      2001 | 507.81273  1027.4596 | 679.76399
           | 564.18529  913.58706 | 740.41329
           |       550        272 |       822
-----------+----------------------+----------
      2002 | 553.89625  1245.1105 | 782.96917
           | 539.21861    1010.94 | 798.53536
           |       347        172 |       519
-----------+----------------------+----------
      2003 | 418.90515  1219.4357 | 639.09037
           | 560.05473  1165.2873 | 852.52805
           |       369        140 |       509
-----------+----------------------+----------
      2004 | 624.86129  1702.8515 | 889.76886
           | 811.57264  1269.1878 | 1051.2857
           |       310        101 |       411
-----------+----------------------+----------
      2005 | 613.85551  1200.4643 | 755.85879
           | 831.70328  1378.8593 |  1021.369
           |       263         NA |       347
-----------+----------------------+----------
      2006 | 581.15584  1595.9692 | 804.00338
           | 853.75194  1531.2897 | 1119.8606
           |       231         NA |       296
-----------+----------------------+----------
      2007 | 450.20588  1664.9783 |   673.724
           | 721.31101  1640.1714 | 1064.3302
           |       204         NA |       250
-----------+----------------------+----------
      2008 | 667.09444  1696.4792 | 883.80702
           | 1133.5017  1841.6236 | 1375.5936
           |       180         NA |       228
-----------+----------------------+----------
      2009 | 687.58182   2295.303 | 1058.5944
           | 1173.0878  2383.8239 | 1673.0165
           |       110         NA |       143
-----------+----------------------+----------
      2010 | 551.09322  3251.1053 | 1208.7885
           | 1093.5339  2455.6102 |  1921.968
           |       118         NA |       156
-----------+----------------------+----------
      2011 | 457.49383  2267.7826 | 857.84615
           | 860.58659  2502.4562 |  1575.677
           |        NA         NA |       104
-----------+----------------------+----------
      2012 | 256.01493  813.41176 | 368.82143
           | 719.27806   1788.639 | 1038.6873
           |        NA         NA |        NA
-----------+----------------------+----------
      2013 | 335.36066  551.63636 | 368.40278
           | 536.93817  1547.1943 | 766.11492
           |        NA         NA |        NA
-----------+----------------------+----------
      2014 |     541.6      147.5 | 461.63768
           | 1171.1106  212.26063 | 1059.8275
           |        NA         NA |        NA
-----------+----------------------+----------
      2015 | 797.28302  1672.2727 | 947.67188
           | 1715.6879  2520.7049 | 1883.8501
           |        NA         NA |        NA
-----------+----------------------+----------
      2016 |  263.4902      760.5 | 358.15873
           | 592.78311  1772.9982 | 937.97954
           |        NA         NA |        NA
-----------+----------------------+----------
      2017 | 384.46296      207.6 | 356.82813
           |  762.8161  136.61479 | 704.54294
           |        NA         NA |        NA
-----------+----------------------+----------
     Total | 385.59393  762.06591 | 501.15899
           | 471.70564   779.6285 | 609.03183
           |     18120       8026 |     26146
r; t=15.75 14:47:59

.                         tabulate frau if (posttrans==11 | posttrans==12), summarize(dur) 

            |  Summary of duration in labour mkt
            |               status
       frau |        Mean   Std. dev.       Freq.
------------+------------------------------------
       mann |   251.63106   420.79551     139,493
       frau |   439.68429   659.28343      36,765
------------+------------------------------------
      Total |   290.85639   486.44981     176,258
r; t=1.10 14:48:00

.                         tabulate jahrend if (posttrans==11 | posttrans==12), summarize(dur) 

            |  Summary of duration in labour mkt
year at end |               status
   of spell |        Mean   Std. dev.       Freq.
------------+------------------------------------
       1975 |          10           0          NA
       1976 |   66.012195   87.638778          NA
       1977 |   81.230964   89.595301         394
       1978 |   90.096899   104.64014         258
       1979 |   99.080717   125.10062         223
       1980 |   112.27027   157.23051         296
       1981 |   116.18926   154.59039         391
       1982 |   144.35845   171.13018         438
       1983 |   175.84534   188.09864         472
       1984 |    159.2082   198.24768         634
       1985 |   163.51958   210.44936         664
       1986 |   155.97342   192.10023         489
       1987 |   168.79221   234.37992         385
       1988 |   161.51057   216.75076         331
       1989 |    175.1987   237.03472         307
       1990 |   147.85274   238.18547         292
       1991 |   151.34286   375.76194         315
       1992 |   89.861502   124.02738         852
       1993 |   127.23919   129.25153       2,220
       1994 |   148.41425   159.92838       3,522
       1995 |   178.50823   208.66268       4,193
       1996 |   186.72626   216.65922       7,562
       1997 |   201.30963   227.62119       9,744
       1998 |    257.8853   288.84088      11,081
       1999 |   266.76538   325.20277      10,941
       2000 |   283.64486   351.87405      10,855
       2001 |   283.90393    358.8273      10,742
       2002 |   293.31619    367.0953      10,032
       2003 |   288.89411   376.59819       9,633
       2004 |   345.45428   483.21113       9,690
       2005 |   317.21097   480.71382       8,788
       2006 |   351.06101   524.76975       8,785
       2007 |   388.36995   618.03736       7,174
       2008 |   428.97303   709.09952       6,451
       2009 |   347.80399   654.20295       6,260
       2010 |   344.10377   622.48057       6,264
       2011 |   359.65663   704.88778       5,114
       2012 |     300.422   633.93122       4,064
       2013 |   303.97629   667.26385       4,049
       2014 |   369.84228   801.49879       3,690
       2015 |   362.81714   811.60388       3,325
       2016 |   337.78672    790.4251       2,921
       2017 |   351.70137   850.36007       2,334
------------+------------------------------------
      Total |   290.85639   486.44981     176,258
r; t=1.18 14:48:01

.                         tabulate jahrend frau if (posttrans==11 | posttrans==12), summarize(dur)                

                Means, Standard Deviations and Frequencies
                     of duration in labour mkt status

   year at |
    end of |        frau
     spell |      mann       frau |     Total
-----------+----------------------+----------
      1975 |        10          . |        10
           |         0          . |         0
           |         1          0 |         1
-----------+----------------------+----------
      1976 | 62.192308      140.5 | 66.012195
           | 85.128788  116.59188 | 87.638778
           |        NA         NA |        NA
-----------+----------------------+----------
      1977 | 80.013333  105.26316 | 81.230964
           | 87.952937  117.86237 | 89.595301
           |       375         19 |       394
-----------+----------------------+----------
      1978 | 89.359833  99.368421 | 90.096899
           | 102.22884  134.37064 | 104.64014
           |       239         19 |       258
-----------+----------------------+----------
      1979 | 98.082524  111.17647 | 99.080717
           | 127.10279  99.951385 | 125.10062
           |       206         17 |       223
-----------+----------------------+----------
      1980 | 108.60448  147.35714 | 112.27027
           | 157.36872  154.26269 | 157.23051
           |       268         28 |       296
-----------+----------------------+----------
      1981 | 117.39155  104.33333 | 116.18926
           | 158.18918  114.17906 | 154.59039
           |       355         36 |       391
-----------+----------------------+----------
      1982 | 145.70488  124.64286 | 144.35845
           | 173.82416  125.94597 | 171.13018
           |       410         28 |       438
-----------+----------------------+----------
      1983 | 179.31797  136.18421 | 175.84534
           | 192.59661  120.58682 | 188.09864
           |       434         38 |       472
-----------+----------------------+----------
      1984 | 157.26633  190.54054 |  159.2082
           | 197.36033  212.44785 | 198.24768
           |       597         37 |       634
-----------+----------------------+----------
      1985 | 164.80952  139.61765 | 163.51958
           | 211.98937  180.58407 | 210.44936
           |       630         34 |       664
-----------+----------------------+----------
      1986 | 152.65934  200.32353 | 155.97342
           | 187.24563  247.34257 | 192.10023
           |       455         34 |       489
-----------+----------------------+----------
      1987 | 166.74857  189.22857 | 168.79221
           | 234.53612  235.21243 | 234.37992
           |       350         35 |       385
-----------+----------------------+----------
      1988 | 162.95548  150.69231 | 161.51057
           | 217.14282  216.28309 | 216.75076
           |       292         39 |       331
-----------+----------------------+----------
      1989 | 182.91321  126.52381 |  175.1987
           |  251.1398  102.01321 | 237.03472
           |       265         42 |       307
-----------+----------------------+----------
      1990 |  156.3622  90.973684 | 147.85274
           | 251.93778  91.414555 | 238.18547
           |       254         38 |       292
-----------+----------------------+----------
      1991 | 140.67018  252.73333 | 151.34286
           | 281.89956   859.5563 | 375.76194
           |       285         30 |       315
-----------+----------------------+----------
      1992 | 91.017128  80.430108 | 89.861502
           | 127.81045  87.175036 | 124.02738
           |       759         93 |       852
-----------+----------------------+----------
      1993 | 125.94652  134.14571 | 127.23919
           | 126.15901  144.66158 | 129.25153
           |      1870        350 |      2220
-----------+----------------------+----------
      1994 | 137.63677  197.50789 | 148.41425
           | 150.52193  189.71619 | 159.92838
           |      2888        634 |      3522
-----------+----------------------+----------
      1995 | 157.94114  245.75967 | 178.50823
           | 191.90186  244.21554 | 208.66268
           |      3211        982 |      4193
-----------+----------------------+----------
      1996 |  160.8002  299.74628 | 186.72626
           | 190.61114  278.44439 | 216.65922
           |      6151       1411 |      7562
-----------+----------------------+----------
      1997 | 175.30869  323.46842 | 201.30963
           | 194.57291  315.14431 | 227.62119
           |      8034       1710 |      9744
-----------+----------------------+----------
      1998 | 217.85327  408.23734 |  257.8853
           | 247.72896   371.0858 | 288.84088
           |      8751       2330 |     11081
-----------+----------------------+----------
      1999 | 227.90138   407.0885 | 266.76538
           | 276.93641  430.82988 | 325.20277
           |      8568       2373 |     10941
-----------+----------------------+----------
      2000 | 243.34175  421.40049 | 283.64486
           | 304.84358   452.6861 | 351.87405
           |      8398       2457 |     10855
-----------+----------------------+----------
      2001 | 248.83151  409.56015 | 283.90393
           | 310.55386    473.723 |  358.8273
           |      8398       2344 |     10742
-----------+----------------------+----------
      2002 |  253.9437  447.65866 | 293.31619
           | 312.04078  501.53226 |  367.0953
           |      7993       2039 |     10032
-----------+----------------------+----------
      2003 | 257.21599  427.69385 | 288.89411
           | 326.68649  521.63067 | 376.59819
           |      7843       1790 |      9633
-----------+----------------------+----------
      2004 | 293.31571  551.74169 | 345.45428
           | 410.37155  661.73749 | 483.21113
           |      7735       1955 |      9690
-----------+----------------------+----------
      2005 | 276.15291  492.73049 | 317.21097
           | 422.55821  646.41623 | 480.71382
           |      7122       1666 |      8788
-----------+----------------------+----------
      2006 | 319.62201  476.45997 | 351.06101
           |  483.0925  650.74978 | 524.76975
           |      7024       1761 |      8785
-----------+----------------------+----------
      2007 | 345.11578  530.70514 | 388.36995
           | 547.71321  790.98035 | 618.03736
           |      5502       1672 |      7174
-----------+----------------------+----------
      2008 | 376.83409  588.77379 | 428.97303
           | 644.94594  857.67199 | 709.09952
           |      4864       1587 |      6451
-----------+----------------------+----------
      2009 | 289.36284  542.01588 | 347.80399
           | 544.16062  904.22353 | 654.20295
           |      4812       1448 |      6260
-----------+----------------------+----------
      2010 | 296.79891  523.68836 | 344.10377
           | 517.16654  896.04527 | 622.48057
           |      4958       1306 |      6264
-----------+----------------------+----------
      2011 | 315.84227  497.41977 | 359.65663
           | 628.29174  890.78118 | 704.88778
           |      3880       1234 |      5114
-----------+----------------------+----------
      2012 | 255.33587  432.19884 |   300.422
           | 541.87002   833.9709 | 633.93122
           |      3028       1036 |      4064
-----------+----------------------+----------
      2013 | 258.91163  446.82801 | 303.97629
           | 577.84226  878.64386 | 667.26385
           |      3078        971 |      4049
-----------+----------------------+----------
      2014 | 301.10503  568.65823 | 369.84228
           |  679.2096  1055.4296 | 801.49879
           |      2742        948 |      3690
-----------+----------------------+----------
      2015 | 302.84919  538.81775 | 362.81714
           | 695.43329  1064.0149 | 811.60388
           |      2480        845 |      3325
-----------+----------------------+----------
      2016 | 296.15186   458.7393 | 337.78672
           | 703.14357   992.4505 |  790.4251
           |      2173        748 |      2921
-----------+----------------------+----------
      2017 |  331.7772  409.67169 | 351.70137
           | 825.34214  917.55788 | 850.36007
           |      1737        597 |      2334
-----------+----------------------+----------
     Total | 251.63106  439.68429 | 290.85639
           | 420.79551  659.28343 | 486.44981
           |    139493      36765 |    176258
r; t=19.18 14:48:20

. 
.                 *******************************************************************
.                 ********(4.0.b ) Diagnostics on (very) short spells ***************                     
.                 *******************************************************************
. 
.                         tab status if dur<7 & dur!=.

                      status |      Freq.     Percent        Cum.
-----------------------------+-----------------------------------
                  unemployed |      8,974       38.80       38.80
            active_labour_mp |        685        2.96       41.76
         marginal_employment |      1,872        8.09       49.85
normal_employment_(FT_or_PT) |      8,621       37.27       87.12
         vocational_training |      2,978       12.88      100.00
-----------------------------+-----------------------------------
                       Total |     23,130      100.00
r; t=0.33 14:48:20

.                         tab status if dur<30 & dur!=.

                      status |      Freq.     Percent        Cum.
-----------------------------+-----------------------------------
                  unemployed |     36,200       40.48       40.48
            active_labour_mp |      1,731        1.94       42.42
         marginal_employment |      5,520        6.17       48.59
normal_employment_(FT_or_PT) |     31,204       34.89       83.48
         vocational_training |     10,614       11.87       95.35
                          10 |      4,156        4.65      100.00
-----------------------------+-----------------------------------
                       Total |     89,425      100.00
r; t=0.32 14:48:21

. 
.                         su dur if dur<30 & dur!=. & (status == 4 | status == 1 | status == 0), det

                duration in labour mkt status
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            1              1
10%            2              1       Obs              48,545
25%            6              1       Sum of wgt.      48,545

50%           14                      Mean           13.90835
                        Largest       Std. dev.      8.511692
75%           21             29
90%           26             29       Variance        72.4489
95%           28             29       Skewness       .1154888
99%           29             29       Kurtosis       1.801515
r; t=1.14 14:48:22

.                         su dur if dur<30 & dur!=. & (status == 3 | status == 2), det

                duration in labour mkt status
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            1              1
10%            2              1       Obs              36,724
25%            5              1       Sum of wgt.      36,724

50%           13                      Mean           13.73167
                        Largest       Std. dev.      8.925202
75%           22             29
90%           26             29       Variance       79.65923
95%           28             29       Skewness       .1405604
99%           29             29       Kurtosis       1.732165
r; t=1.18 14:48:23

.                         
.                         * pre & post short spells
.                         g short=1 if dur<7 & dur!=.
(1,313,276 missing values generated)
r; t=0.06 14:48:23

.                         
.                         * what is BEFORE short spells?          
.                         g preshort=status[_n-1] if pid==pid[_n-1] & short==1
(1,313,704 missing values generated)
r; t=0.50 14:48:24

.                         tab preshort

   preshort |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      6,302       27.76       27.76
          1 |        313        1.38       29.14
          2 |        351        1.55       30.68
          3 |      9,342       41.15       71.84
          4 |      2,792       12.30       84.13
         10 |      3,602       15.87      100.00
------------+-----------------------------------
      Total |     22,702      100.00
r; t=0.13 14:48:24

.                         * what is before short unemp spells?
.                         tab preshort if status==0 & short==1

   preshort |      Freq.     Percent        Cum.
------------+-----------------------------------
          3 |      7,202       81.38       81.38
          4 |        255        2.88       84.26
         10 |      1,393       15.74      100.00
------------+-----------------------------------
      Total |      8,850      100.00
r; t=0.31 14:48:24

.                         * what is before short emp spells?
.                         tab preshort if (status == 3 | status == 2) & short==1

   preshort |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      6,115       59.57       59.57
          1 |        293        2.85       62.43
          2 |        231        2.25       64.68
          3 |      1,561       15.21       79.88
          4 |        114        1.11       80.99
         10 |      1,951       19.01      100.00
------------+-----------------------------------
      Total |     10,265      100.00
r; t=0.46 14:48:25

. 
.                         * what is AFTER short spells?                   
.                         g postshort=status[_n+1] if pid==pid[_n+1] & short==1
(1,313,948 missing values generated)
r; t=0.41 14:48:25

.                         tab postshort

  postshort |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      6,691       29.79       29.79
          1 |        411        1.83       31.62
          2 |        441        1.96       33.59
          3 |      9,746       43.40       76.98
          4 |      1,853        8.25       85.23
         10 |      3,316       14.77      100.00
------------+-----------------------------------
      Total |     22,458      100.00
r; t=0.13 14:48:25

.                         * what is after short unemp spells?
.                         tab postshort if status==0 & short==1

  postshort |      Freq.     Percent        Cum.
------------+-----------------------------------
          3 |      7,722       87.83       87.83
          4 |         NA        0.69       88.52
         10 |      1,009       11.48      100.00
------------+-----------------------------------
      Total |      8,792      100.00
r; t=0.32 14:48:25

.                         * what is after short emp spells?
.                         tab postshort if (status == 3 | status == 2) & short==1

  postshort |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      5,906       58.84       58.84
          1 |        371        3.70       62.53
          2 |        339        3.38       65.91
          3 |      1,294       12.89       78.80
          4 |         NA        0.84       79.64
         10 |      2,044       20.36      100.00
------------+-----------------------------------
      Total |     10,038      100.00
r; t=0.48 14:48:26

. 
. 
. /* ------------------------------------------------------------------------ */
.  *  (5) First estimation on the whole sample
. /* ------------------------------------------------------------------------ */  
.                  * 5.1 We calculate estimator for the whole sample before cutting spells that cross macro scenario (make sense only for estimation by cell)     
.                  * 5.2 Same     but taking into account right censoring due to black_hole and end of spell 
.  
.         /* ------------------------------------------------------------------------ */
.          *  (5.1) Without taking into account any type of censoring
.         /* ------------------------------------------------------------------------ */
.                         
.         * We calculate estimator for the whole sample before cutting spells that cross macro scenario (make sense only for estimation by cell)
.         * Probability of transition = (1/duration of status)            ( -> Poisson rate)
. 
.         /* Different possibilities of scenarios in the loop to test excluding outliers
>                 - 0 : use whole sample
>                 - 0.01 : exclude bottom and top outliers of the duration distribution at percentile 0.01
>                 - 1 : exclude bottom and top outliers of the duration distribution at percentile 1
>                 - 2 : exclude bottom and top outliers of the duration distribution at percentile 2
>                 - 3 : excluding 1 day duration spells
>                 - 4 : excluding 1 to 6 days duration spells
>                 - 5 : excluding 1% top duration spells
>                 - 6 : excluding 2% top duration spells
>                 - 7 : excluding all observations of people who have at least 1 day spell
>         */
.         
.         foreach x in 0 /*0.01 0.1 1 2 3 4 5 6 7*/ {
  2.         
.         use ${data}\postcoll.dta, clear
  3.         
.         cap drop keep
  4.         gen keep=1
  5. 
.         * Whole sample
.         if  `x'==0 {
  6.         di "Analysis on the whole sample"
  7.         }
  8.                 
.         * Removing spells at top and bottom percentile of distribution  
.         if `x'>0 & `x'<3 {
  9.         di "Analysis excluding bottom and top outliers of the duration distribution at the p`x'"        
 10.         
.         local top= 100-`x'
 11.         _pctile dur, p(`x' `top')
 12.         return list
 13.         local threshold_bottom = `r(r1)'        
 14.         local threshold_top = `r(r2)'   
 15.         replace keep=0 if dur<`threshold_bottom' | dur>`threshold_top'
 16.         }
 17.         
.         * Removing 1 day duration spells
.         if `x'==3 {
 18.         di "Analysis excluding 1 day duration spells"
 19.         replace keep=0 if dur==1
 20.         }       
 21.         
.         * Removing  1 to 6 day duration spells
.         if `x'==4 {
 22.         di "Analysis excluding 1 to 6 day duration spells"
 23.         replace keep=0 if dur<7
 24.         }       
 25.         
.         * Removing 1% top distribution spells
.         if `x'==5 {
 26.         di "Analysis excluding 1% top distribution spells"
 27.         _pctile dur, p(99)
 28.         return list
 29.         local threshold_top = `r(r1)'   
 30.         replace keep=0 if dur>`threshold_top'
 31.         }
 32.         
.         * Removing 2% top distribution spells
.         if `x'==6 {
 33.         di "Analysis excluding 2% top distribution spells"
 34.         _pctile dur, p(98)
 35.         return list
 36.         local threshold_top = `r(r1)'   
 37.         replace keep=0 if dur>`threshold_top'
 38.         }
 39.         
.         * Removing all observations of people who have at least 1 day spell
.         if `x'==7 {
 40.         di "Analysis excluding all observations of people who have at least 1 day spell"
 41.         cap drop shortdur
 42.         gen shortdur=0
 43.         bysort persnr (begepi): replace shortdur=1 if dur==1
 44.         tab shortdur
 45.         *copy values to all observations
.         cap drop indivshort
 46.         bysort persnr (begepi): egen indivshort=max(shortdur)
 47.         tab indivshort
 48.         replace keep=0 if indivshort==1
 49.         }
 50.         
.         tab keep
 51.         sum dur if keep==0, detail
 52.         tab pretrans keep
 53.         tab posttrans keep      
 54.         drop if keep==0 
 55.         sum dur, detail 
 56.         
.                 *  (5.1.a)  Retirement probability rho
.                         
.                 *       We estimate three different rhos (ex-mu's:)
.                 *       1) do not distinguish between those two types of retirement
.                 *       2) rho (ex-mu) direct retirement probability  (pretrans == 1)   -> lignite -> retirement
.                 *       3) rho (ex-mu) indirect retirement probability (pretrans == 6)  -> lignite -> unemp/marg emp/ALMP -> retirement
.                 
.                 di "Lignite leavers' average job duration retirement = rho"
 57.                 capture noisily estpost sum dur if (pretrans==1)
 58.                 if _rc!=2000{
 59.                         putexcel set results/${samplefolder}/5_sample${sample}_wholesample_estimates_NoCensoring, sheet("rhodirect") modify
 60.                         putexcel A2=("Lignite leavers' average job duration before direct retirement") C1=("nb spells") D1=("duration") C2=matrix(e(count)) D2=matrix(e(mean)) 
 61.                         `putexcelclose'
 62.                         if ${iab}==1{
 63.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesample_estimates_NoCensoring, sheet("rhodirect") modify
 64.                                 putexcel B1=("rhodirect") B2=formula(=1/D2)
 65.                                 `putexcelclose'
 66.                         }
 67.                 }       
 68.                 capture noisily estpost sum dur if (pretrans==6)
 69.                 if _rc!=2000{
 70.                         putexcel set results/${samplefolder}/5_sample${sample}_wholesample_estimates_NoCensoring, sheet("rhoindirect") modify
 71.                         putexcel A2=("Lignite leavers' average job duration before indirect retirement") C1=("nb spells") D1=("duration") C2=matrix(e(count)) D2=matrix(e(mean))
 72.                         `putexcelclose'
 73.                         if ${iab}==1{
 74.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesample_estimates_NoCensoring, sheet("rhoindirect") modify
 75.                                 putexcel B1=("rhoindirect") B2=formula(=1/D2)
 76.                                 `putexcelclose'
 77.                         }
 78.                 }
 79.                 capture noisily estpost sum dur if (pretrans==1 | pretrans == 6)
 80.                 if _rc!=2000{
 81.                         putexcel set results/${samplefolder}/5_sample${sample}_wholesample_estimates_NoCensoring, sheet("rho") modify
 82.                         putexcel A2=("Lignite leavers' average job duration before retirement") C1=("nb spells") D1=("duration") C2=matrix(e(count)) D2=matrix(e(mean))  
 83.                         `putexcelclose'
 84.                         if ${iab}==1{
 85.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesample_estimates_NoCensoring, sheet("rho") modify
 86.                                 putexcel B1=("rho") B2=formula(=1/D2)
 87.                                 `putexcelclose'
 88.                         }
 89.                 }
 90.                 
.                 *  (5.1.b) Job loss rate delta
.                 
.                 * 1) deltalig           -> Duration of lignite normal emp => that ends in unemployment, if unemployment ends in any employment later (pretrans 7 and 8)
.                 * 2) deltanonlig        -> Duration of non-lignite normal emp => that ends in unemployment, if unemployment ends in any employment later (pretrans 11 and 12)
. 
.                 di "Lignite leavers' average job duration before unemployment"
 91.                 capture noisily estpost sum dur if (pretrans==7 | pretrans == 8)
 92.                 if _rc!=2000{
 93.                         putexcel set results/${samplefolder}/5_sample${sample}_wholesample_estimates_NoCensoring, sheet("deltalig") modify
 94.                         putexcel A2=("Lignite leavers' average job duration in lignite before unemployment") C1=("nb spells") D1=("duration") C2=matrix(e(count)) D2=matrix(e(mean))
 95.                         `putexcelclose'
 96.                         if ${iab}==1{
 97.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesample_estimates_NoCensoring, sheet("deltalig") modify
 98.                                 putexcel B1=("deltalig") B2=formula(=1/D2)
 99.                                 `putexcelclose'
100.                         }
101.                 }               
102.                 capture noisily estpost sum dur if (pretrans==11 | pretrans == 12)
103.                 if _rc!=2000{
104.                         putexcel set results/${samplefolder}/5_sample${sample}_wholesample_estimates_NoCensoring, sheet("deltanonlig") modify
105.                         putexcel A2=("Lignite leavers' average job duration in NON lignite before unemployment") C1=("nb spells") D1=("duration") C2=matrix(e(count)) D2=matrix(e(mean)) 
106.                         `putexcelclose'
107.                         if ${iab}==1{
108.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesample_estimates_NoCensoring, sheet("deltanonlig") modify
109.                                 putexcel B1=("deltanonlig") B2=formula(=1/D2)
110.                                 `putexcelclose'
111.                         }
112.                 }
113.                 
.                 *  (5.1.c) Job offer arrival rate / Job finding rate lambda
.         
.                 * 1) lambdalig          -> finding a job in lignite if unemployed (=0 by assumption) after lignite - posttrans = 8 (lignite - unemp/ALMP/marg - job in lignite)
.                 * 2) lambdanonlig       -> finding a job in non-lignite if unemployed after lignite - posttrans = 7 (lignite - unemp/ALMP/marg - job in non-lignite)
.                 * 3) lambdazerolig      -> finding a job in non-lignite if unemployed after non-lignite - posttrans = 11 (non-lignite - unemp/ALMP/marg - job in non-lignite)   
.                                  
.         
.                 di "Lignite leavers' average unemployment duration (ending in a new lignite or non-lignite job)"
114.                 capture noisily estpost sum dur if  posttrans==7
115.                 if _rc!=2000{
116.                         putexcel set results/${samplefolder}/5_sample${sample}_wholesample_estimates_NoCensoring, sheet("lambdanonlig") modify
117.                         putexcel A2=("Lignite leavers' average unemployment duration ending in a NON lignite job") C1=("nb spells") D1=("duration") C2=matrix(e(count)) D2=matrix(e(mean)) 
118.                         `putexcelclose'
119.                         if ${iab}==1{
120.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesample_estimates_NoCensoring, sheet("lambdanonlig") modify
121.                                 putexcel B1=("lambdanonlig") B2=formula(=1/D2)
122.                                 `putexcelclose'
123.                         }
124.                 }
125.                 capture noisily estpost sum dur if  posttrans==8
126.                 if _rc!=2000{
127.                         putexcel set results/${samplefolder}/5_sample${sample}_wholesample_estimates_NoCensoring, sheet("lambdalig") modify
128.                         putexcel A2=("Lignite leavers' average unemployment duration ending in a lignite job") C1=("nb spells") D1=("duration") C2=matrix(e(count)) D2=matrix(e(mean)) 
129.                         `putexcelclose'
130.                         if ${iab}==1{
131.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesample_estimates_NoCensoring, sheet("lambdalig") modify
132.                                 putexcel B1=("lambdalig") B2=formula(=1/D2)
133.                                 `putexcelclose'
134.                         }
135.                 }
136.                 
.                 di "NON Lignite leavers' average unemployment duration (ending in a NON-lignite job)"
137.                 capture noisily estpost sum dur if  posttrans==11
138.                 if _rc!=2000{
139.                         putexcel set results/${samplefolder}/5_sample${sample}_wholesample_estimates_NoCensoring, sheet("lambdazerolig") modify
140.                         putexcel A2=("NON Lignite leavers' average unemployment duration ending in a NON lignite job") C1=("nb spells") D1=("duration") C2=matrix(e(count)) D2=matrix(e(mean)) 
141.                         `putexcelclose'
142.                         if ${iab}==1{
143.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesample_estimates_NoCensoring, sheet("lambdazerolig") modify
144.                                 putexcel B1=("lambdazerolig") B2=formula(=1/D2)
145.                                 `putexcelclose'
146.                         }
147.                 }
148.         }       
Analysis on the whole sample

       keep |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |  1,336,406      100.00      100.00
------------+-----------------------------------
      Total |  1,336,406      100.00

                duration in labour mkt status
-------------------------------------------------------------
no observations

                      |    keep
             pretrans |         1 |     Total
----------------------+-----------+----------
Not pre (observed) tr | 1,033,774 | 1,033,774 
pre retirement (witho |     9,443 |     9,443 
pre trans'n 2 vocatio |       159 |       159 
pre trans'n 2 other n |    24,182 |    24,182 
pre trans'n to unem/A |    14,709 |    14,709 
pre trans'n 2 unemp/A |    22,273 |    22,273 
pre trans'n 2 unemp/A |     3,873 |     3,873 
pre trans'n 2 black h |    51,735 |    51,735 
pre transition out of |   171,765 |   171,765 
pre transition out of |     4,493 |     4,493 
----------------------+-----------+----------
                Total | 1,336,406 | 1,336,406 

                      |    keep
            posttrans |         1 |     Total
----------------------+-----------+----------
Not post trans'n out  | 1,036,085 | 1,036,085 
in retirement (no min |       317 |       317 
 post-lignite vocatio |       158 |       158 
post-lignite normal e |    30,998 |    30,998 
post-lignite unemp/AL |    14,709 |    14,709 
post-lignite unemp/AL |    22,273 |    22,273 
post-lignite unemp/AL |     3,873 |     3,873 
post-lignite black ho |    51,735 |    51,735 
transition out of NON |   171,765 |   171,765 
transition out of NON |     4,493 |     4,493 
----------------------+-----------+----------
                Total | 1,336,406 | 1,336,406 
(0 observations deleted)

                duration in labour mkt status
-------------------------------------------------------------
      Percentiles      Smallest
 1%            3              1
 5%           23              1
10%           35              1       Obs           1,336,406
25%           92              1       Sum of wgt.   1,336,406

50%          300                      Mean           792.4765
                        Largest       Std. dev.      1414.145
75%          771          15609
90%         2070          15691       Variance        1999805
95%         3528          15704       Skewness       3.806334
99%         7305          15706       Kurtosis       21.91888
Lignite leavers' average job duration retirement = rho

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         dur |      9443       9443   3725.876    9079266   3013.182          1      15584   3.52e+07 
file results/two/5_sample2_wholesample_estimates_NoCensoring.xlsx saved
file results/two/5_sample2_wholesample_estimates_NoCensoring.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         dur |     14709      14709   2450.923    5922591   2433.637          1      14610   3.61e+07 
file results/two/5_sample2_wholesample_estimates_NoCensoring.xlsx saved
file results/two/5_sample2_wholesample_estimates_NoCensoring.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         dur |     24152      24152   2949.407    7543543   2746.551          1      15584   7.12e+07 
file results/two/5_sample2_wholesample_estimates_NoCensoring.xlsx saved
file results/two/5_sample2_wholesample_estimates_NoCensoring.xlsx saved
Lignite leavers' average job duration before unemployment

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         dur |     26146      26146   875.5382    1148039   1071.466          1      15432   2.29e+07 
file results/two/5_sample2_wholesample_estimates_NoCensoring.xlsx saved
file results/two/5_sample2_wholesample_estimates_NoCensoring.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         dur |    176258     176258   621.0402     949151   974.2438          1      14338   1.09e+08 
file results/two/5_sample2_wholesample_estimates_NoCensoring.xlsx saved
file results/two/5_sample2_wholesample_estimates_NoCensoring.xlsx saved
Lignite leavers' average unemployment duration (ending in a new lignite or non-lignite job)

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         dur |     22273      22273   556.7482   404051.2   635.6502          1       7397   1.24e+07 
file results/two/5_sample2_wholesample_estimates_NoCensoring.xlsx saved
file results/two/5_sample2_wholesample_estimates_NoCensoring.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         dur |      3873       3873   181.4743   60441.04   245.8476          1       3957     702850 
file results/two/5_sample2_wholesample_estimates_NoCensoring.xlsx saved
file results/two/5_sample2_wholesample_estimates_NoCensoring.xlsx saved
NON Lignite leavers' average unemployment duration (ending in a NON-lignite job)

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         dur |    171765     171765   291.9728   239997.2   489.8951          1       7716   5.02e+07 
file results/two/5_sample2_wholesample_estimates_NoCensoring.xlsx saved
file results/two/5_sample2_wholesample_estimates_NoCensoring.xlsx saved
r; t=28.47 14:48:54

.                 
.         
.         /* ------------------------------------------------------------------------ */
.          *  (5.2) taking into account right censoring due to black_hole and end of spell 
.         /* ------------------------------------------------------------------------ */
.         /*ASSUMPTION : constant risk over time  
>         CENSORING : waiting time > observation time
>         METHOD :        we use the maximum likelihod estimate of the hazard =  [ total number of transitions / total exposure time ]
>                 
>         Identify general right censoring due to no observation of transition
>         2 cases
>         - right censoring due to last spell
>         - right censoring due to black hole
>         */
.                 
.                 use ${data}\postcoll.dta, clear 
r; t=17.60 14:49:12

.                 
.                 *Identify last spell
.                 cap drop last_spell1
r; t=0.00 14:49:12

.                 gen last_spell1=0
r; t=0.04 14:49:12

.                 by pid (begepi), sort: replace last_spell1 = (_n==_N)
(146916 real changes made)
r; t=0.07 14:49:12

.                 tab last_spell1         

last_spell1 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  1,189,490       89.01       89.01
          1 |    146,916       10.99      100.00
------------+-----------------------------------
      Total |  1,336,406      100.00
r; t=0.22 14:49:12

.                 tab pretrans last_spell1

                      |      last_spell1
             pretrans |         0          1 |     Total
----------------------+----------------------+----------
Not pre (observed) tr |   895,983    137,791 | 1,033,774 
pre retirement (witho |       318      9,125 |     9,443 
pre trans'n 2 vocatio |       159          0 |       159 
pre trans'n 2 other n |    24,182          0 |    24,182 
pre trans'n to unem/A |    14,709          0 |    14,709 
pre trans'n 2 unemp/A |    22,273          0 |    22,273 
pre trans'n 2 unemp/A |     3,873          0 |     3,873 
pre trans'n 2 black h |    51,735          0 |    51,735 
pre transition out of |   171,765          0 |   171,765 
pre transition out of |     4,493          0 |     4,493 
----------------------+----------------------+----------
                Total | 1,189,490    146,916 | 1,336,406 
r; t=0.40 14:49:13

.                 tab posttrans last_spell1

                      |      last_spell1
            posttrans |         0          1 |     Total
----------------------+----------------------+----------
Not post trans'n out  |   905,662    130,423 | 1,036,085 
in retirement (no min |        NA        260 |       317 
 post-lignite vocatio |       154         NA |       158 
post-lignite normal e |    27,969      3,029 |    30,998 
post-lignite unemp/AL |     1,509     13,200 |    14,709 
post-lignite unemp/AL |    22,273          0 |    22,273 
post-lignite unemp/AL |     3,873          0 |     3,873 
post-lignite black ho |    51,735          0 |    51,735 
transition out of NON |   171,765          0 |   171,765 
transition out of NON |     4,493          0 |     4,493 
----------------------+----------------------+----------
                Total | 1,189,490    146,916 | 1,336,406 
r; t=0.40 14:49:13

.                 *caution: transition to retirement are not last spell
.                 replace last_spell1=0 if (posttrans==1 | posttrans==6 | pretrans==1 | pretrans==6)
(22,577 real changes made)
r; t=0.05 14:49:13

.                 tab last_spell1         

last_spell1 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  1,212,067       90.70       90.70
          1 |    124,339        9.30      100.00
------------+-----------------------------------
      Total |  1,336,406      100.00
r; t=0.24 14:49:13

.                 tab pretrans last_spell1

                      |      last_spell1
             pretrans |         0          1 |     Total
----------------------+----------------------+----------
Not pre (observed) tr |   909,435    124,339 | 1,033,774 
pre retirement (witho |     9,443          0 |     9,443 
pre trans'n 2 vocatio |       159          0 |       159 
pre trans'n 2 other n |    24,182          0 |    24,182 
pre trans'n to unem/A |    14,709          0 |    14,709 
pre trans'n 2 unemp/A |    22,273          0 |    22,273 
pre trans'n 2 unemp/A |     3,873          0 |     3,873 
pre trans'n 2 black h |    51,735          0 |    51,735 
pre transition out of |   171,765          0 |   171,765 
pre transition out of |     4,493          0 |     4,493 
----------------------+----------------------+----------
                Total | 1,212,067    124,339 | 1,336,406 
r; t=0.41 14:49:14

.                 tab posttrans last_spell1

                      |      last_spell1
            posttrans |         0          1 |     Total
----------------------+----------------------+----------
Not post trans'n out  |   914,779    121,306 | 1,036,085 
in retirement (no min |       317          0 |       317 
 post-lignite vocatio |       154         NA |       158 
post-lignite normal e |    27,969      3,029 |    30,998 
post-lignite unemp/AL |    14,709          0 |    14,709 
post-lignite unemp/AL |    22,273          0 |    22,273 
post-lignite unemp/AL |     3,873          0 |     3,873 
post-lignite black ho |    51,735          0 |    51,735 
transition out of NON |   171,765          0 |   171,765 
transition out of NON |     4,493          0 |     4,493 
----------------------+----------------------+----------
                Total | 1,212,067    124,339 | 1,336,406 
r; t=0.43 14:49:14

. 
.                 *Identify black holes that come next
.                 cap drop black_hole_next1
r; t=0.00 14:49:14

.                 gen black_hole_next1=0
r; t=0.05 14:49:14

.                 by pid (begepi), sort: replace black_hole_next1=1 if (pid[_n+1]==pid & status[_n+1]==10)
(207472 real changes made)
r; t=0.09 14:49:14

.                 tab black_hole_next1

black_hole_ |
      next1 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  1,128,934       84.48       84.48
          1 |    207,472       15.52      100.00
------------+-----------------------------------
      Total |  1,336,406      100.00
r; t=0.22 14:49:15

.                 tab pretrans black_hole_next1

                      |   black_hole_next1
             pretrans |         0          1 |     Total
----------------------+----------------------+----------
Not pre (observed) tr |   878,033    155,741 | 1,033,774 
pre retirement (witho |     9,440         NA |     9,443 
pre trans'n 2 vocatio |       159          0 |       159 
pre trans'n 2 other n |    24,182          0 |    24,182 
pre trans'n to unem/A |    14,708         NA |    14,709 
pre trans'n 2 unemp/A |    22,273          0 |    22,273 
pre trans'n 2 unemp/A |     3,873          0 |     3,873 
pre trans'n 2 black h |        NA     51,727 |    51,735 
pre transition out of |   171,765          0 |   171,765 
pre transition out of |     4,493          0 |     4,493 
----------------------+----------------------+----------
                Total | 1,128,934    207,472 | 1,336,406 
r; t=0.41 14:49:15

.                 tab posttrans black_hole_next1

                      |   black_hole_next1
            posttrans |         0          1 |     Total
----------------------+----------------------+----------
Not post trans'n out  |   835,810    200,275 | 1,036,085 
in retirement (no min |       275         NA |       317 
 post-lignite vocatio |       130         NA |       158 
post-lignite normal e |    25,369      5,629 |    30,998 
post-lignite unemp/AL |    13,211      1,498 |    14,709 
post-lignite unemp/AL |    22,273          0 |    22,273 
post-lignite unemp/AL |     3,873          0 |     3,873 
post-lignite black ho |    51,735          0 |    51,735 
transition out of NON |   171,765          0 |   171,765 
transition out of NON |     4,493          0 |     4,493 
----------------------+----------------------+----------
                Total | 1,128,934    207,472 | 1,336,406 
r; t=0.40 14:49:15

. 
.                 cap drop end1
r; t=0.00 14:49:15

.                 g end1=1
r; t=0.04 14:49:16

.                 replace end1=0 if last_spell1==1 | black_hole_next1==1
(331,811 real changes made)
r; t=0.04 14:49:16

.                 tab end1

       end1 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |    331,811       24.83       24.83
          1 |  1,004,595       75.17      100.00
------------+-----------------------------------
      Total |  1,336,406      100.00
r; t=0.24 14:49:16

.                 tab pretrans end1       

                      |         end1
             pretrans |         0          1 |     Total
----------------------+----------------------+----------
Not pre (observed) tr |   280,080    753,694 | 1,033,774 
pre retirement (witho |         3      9,440 |     9,443 
pre trans'n 2 vocatio |         0        159 |       159 
pre trans'n 2 other n |         0     24,182 |    24,182 
pre trans'n to unem/A |         1     14,708 |    14,709 
pre trans'n 2 unemp/A |         0     22,273 |    22,273 
pre trans'n 2 unemp/A |         0      3,873 |     3,873 
pre trans'n 2 black h |    51,727         NA |    51,735 
pre transition out of |         0    171,765 |   171,765 
pre transition out of |         0      4,493 |     4,493 
----------------------+----------------------+----------
                Total |   331,811  1,004,595 | 1,336,406 
r; t=0.43 14:49:16

.                 tab posttrans end1

                      |         end1
            posttrans |         0          1 |     Total
----------------------+----------------------+----------
Not post trans'n out  |   321,581    714,504 | 1,036,085 
in retirement (no min |        NA        275 |       317 
 post-lignite vocatio |        NA        126 |       158 
post-lignite normal e |     8,658     22,340 |    30,998 
post-lignite unemp/AL |     1,498     13,211 |    14,709 
post-lignite unemp/AL |         0     22,273 |    22,273 
post-lignite unemp/AL |         0      3,873 |     3,873 
post-lignite black ho |         0     51,735 |    51,735 
transition out of NON |         0    171,765 |   171,765 
transition out of NON |         0      4,493 |     4,493 
----------------------+----------------------+----------
                Total |   331,811  1,004,595 | 1,336,406 
r; t=0.42 14:49:17

.                 
.                 *  Exposure time for estimation
.                 
.                 * Identify spells after person enter in 103, keeping date of enter for the spells after entering
.                         cap drop enter103
r; t=0.00 14:49:17

.                         gen enter103=.
(1,336,406 missing values generated)
r; t=0.03 14:49:17

.                         bys persnr (first103): replace enter103=first103[1]
(27530 real changes made)
r; t=0.61 14:49:17

.                         format enter103 %tdDDmonYY
r; t=0.00 14:49:17

.                 
.                 * Identify Employment periods in Lignite 
.                         cap drop Emp_Lig1
r; t=0.00 14:49:17

.                         gen Emp_Lig1=0
r; t=0.04 14:49:17

.                         bysort pid (begepi): replace Emp_Lig1=1 if statsimple==1 & thisspelllignite== 1 
(175152 real changes made)
r; t=0.70 14:49:18

.                         di "check pretrans when employed in lignite"
check pretrans when employed in lignite
r; t=0.00 14:49:18

.                         tab pretrans Emp_Lig1

                      |       Emp_Lig1
             pretrans |         0          1 |     Total
----------------------+----------------------+----------
Not pre (observed) tr |   979,366     54,408 | 1,033,774 
pre retirement (witho |        NA      9,366 |     9,443 
pre trans'n 2 vocatio |         0        159 |       159 
pre trans'n 2 other n |         0     24,182 |    24,182 
pre trans'n to unem/A |         0     14,709 |    14,709 
pre trans'n 2 unemp/A |         0     22,273 |    22,273 
pre trans'n 2 unemp/A |         0      3,873 |     3,873 
pre trans'n 2 black h |     5,553     46,182 |    51,735 
pre transition out of |   171,765          0 |   171,765 
pre transition out of |     4,493          0 |     4,493 
----------------------+----------------------+----------
                Total | 1,161,254    175,152 | 1,336,406 
r; t=0.40 14:49:18

.                 
.                 * Identify Employment periods NOT in Lignite
.                         cap drop Emp_NonLig1
r; t=0.00 14:49:18

.                         gen Emp_NonLig1=0
r; t=0.04 14:49:19

.                         bysort pid (begepi): replace Emp_NonLig1=1 if statsimple==1 & thisspelllignite==0
(436940 real changes made)
r; t=0.09 14:49:19

.                         di "check pretrans when employed in non lignite"
check pretrans when employed in non lignite
r; t=0.00 14:49:19

.                         tab pretrans Emp_NonLig1                

                      |      Emp_NonLig1
             pretrans |         0          1 |     Total
----------------------+----------------------+----------
Not pre (observed) tr |   773,092    260,682 | 1,033,774 
pre retirement (witho |     9,443          0 |     9,443 
pre trans'n 2 vocatio |       159          0 |       159 
pre trans'n 2 other n |    24,182          0 |    24,182 
pre trans'n to unem/A |    14,709          0 |    14,709 
pre trans'n 2 unemp/A |    22,273          0 |    22,273 
pre trans'n 2 unemp/A |     3,873          0 |     3,873 
pre trans'n 2 black h |    51,735          0 |    51,735 
pre transition out of |         0    171,765 |   171,765 
pre transition out of |         0      4,493 |     4,493 
----------------------+----------------------+----------
                Total |   899,466    436,940 | 1,336,406 
r; t=0.40 14:49:19

.                         
.                 * Identify Unemployment periods after Employment in Lignite
.                         cap drop Unemp_postLig1
r; t=0.00 14:49:19

.                         gen Unemp_postLig1=0
r; t=0.03 14:49:19

.                         bysort pid (begepi): replace Unemp_postLig1 =1 if statsimple==0 & statsimple[_n-1]==1 & thisspelllignite[_n-1]== 1
(54119 real changes made)
r; t=0.10 14:49:19

.                         di "check pretrans when unemployed after lignite"
check pretrans when unemployed after lignite
r; t=0.00 14:49:19

.                         tab pretrans Unemp_postLig1                     

                      |    Unemp_postLig1
             pretrans |         0          1 |     Total
----------------------+----------------------+----------
Not pre (observed) tr |   980,996     52,778 | 1,033,774 
pre retirement (witho |     9,439         NA |     9,443 
pre trans'n 2 vocatio |       159          0 |       159 
pre trans'n 2 other n |    24,182          0 |    24,182 
pre trans'n to unem/A |    14,709          0 |    14,709 
pre trans'n 2 unemp/A |    22,273          0 |    22,273 
pre trans'n 2 unemp/A |     3,873          0 |     3,873 
pre trans'n 2 black h |    50,398      1,337 |    51,735 
pre transition out of |   171,765          0 |   171,765 
pre transition out of |     4,493          0 |     4,493 
----------------------+----------------------+----------
                Total | 1,282,287     54,119 | 1,336,406 
r; t=0.40 14:49:20

.                         di "check postrans when unemployed after lignite"
check postrans when unemployed after lignite
r; t=0.00 14:49:20

.                         tab posttrans Unemp_postLig1 

                      |    Unemp_postLig1
            posttrans |         0          1 |     Total
----------------------+----------------------+----------
Not post trans'n out  | 1,023,044     13,041 | 1,036,085 
in retirement (no min |        NA        229 |       317 
 post-lignite vocatio |       158          0 |       158 
post-lignite normal e |    30,998          0 |    30,998 
post-lignite unemp/AL |        NA     14,706 |    14,709 
post-lignite unemp/AL |        NA     22,269 |    22,273 
post-lignite unemp/AL |         0      3,873 |     3,873 
post-lignite black ho |    51,735          0 |    51,735 
transition out of NON |   171,764         NA |   171,765 
transition out of NON |     4,493          0 |     4,493 
----------------------+----------------------+----------
                Total | 1,282,287     54,119 | 1,336,406 
r; t=0.40 14:49:20

. 
.                 * Identify Unemployment periods after Employment NOT in Lignite
.                         cap drop Unemp_postNonLig1
r; t=0.00 14:49:20

.                         g Unemp_postNonLig1=0
r; t=0.04 14:49:20

.                         bysort pid (begepi): replace Unemp_postNonLig1 = 1 if statsimple==0 & statsimple[_n-1]==1 & thisspelllignite[_n-1]== 0  
(228808 real changes made)
r; t=0.10 14:49:20

.                         di "check pretrans when unemployed after non lignite"
check pretrans when unemployed after non lignite
r; t=0.00 14:49:20

.                         tab pretrans Unemp_postNonLig1                  

                      |   Unemp_postNonLig1
             pretrans |         0          1 |     Total
----------------------+----------------------+----------
Not pre (observed) tr |   804,975    228,799 | 1,033,774 
pre retirement (witho |     9,443          0 |     9,443 
pre trans'n 2 vocatio |       159          0 |       159 
pre trans'n 2 other n |    24,182          0 |    24,182 
pre trans'n to unem/A |    14,709          0 |    14,709 
pre trans'n 2 unemp/A |    22,273          0 |    22,273 
pre trans'n 2 unemp/A |     3,873          0 |     3,873 
pre trans'n 2 black h |    51,726         NA |    51,735 
pre transition out of |   171,765          0 |   171,765 
pre transition out of |     4,493          0 |     4,493 
----------------------+----------------------+----------
                Total | 1,107,598    228,808 | 1,336,406 
r; t=0.38 14:49:21

.                         di "check postrans when unemployed after non lignite"
check postrans when unemployed after non lignite
r; t=0.00 14:49:21

.                         tab posttrans Unemp_postNonLig1                         

                      |   Unemp_postNonLig1
            posttrans |         0          1 |     Total
----------------------+----------------------+----------
Not post trans'n out  |   983,533     52,552 | 1,036,085 
in retirement (no min |       317          0 |       317 
 post-lignite vocatio |       158          0 |       158 
post-lignite normal e |    30,998          0 |    30,998 
post-lignite unemp/AL |    14,709          0 |    14,709 
post-lignite unemp/AL |    22,273          0 |    22,273 
post-lignite unemp/AL |     3,873          0 |     3,873 
post-lignite black ho |    51,735          0 |    51,735 
transition out of NON |         2    171,763 |   171,765 
transition out of NON |         0      4,493 |     4,493 
----------------------+----------------------+----------
                Total | 1,107,598    228,808 | 1,336,406 
r; t=0.37 14:49:21

.                 
.                 * for rho : Time in employment in lignite, including ATZ
.                         * When ATZ case (person103==1): end of duration of employment is mid103 instead of endepi
.                         * We then redefine the duration at risk : durretrisk
.                         cap drop durretrisk1
r; t=0.00 14:49:21

.                         gen durretrisk1=dur
r; t=0.06 14:49:21

.                         replace durretrisk1=(mid103-begepi)+1 if person103==1 & mid103>begepi & mid103<endepi
(4,189 real changes made)
r; t=0.07 14:49:21

.                         * spells started after the mid-point of early retirement should not contribute to retirement risk
.                         replace durretrisk1=0 if person103==1 & mid103<begepi
(1,971 real changes made)
r; t=0.06 14:49:21

.                         sum durretrisk1, detail 

                         durretrisk1
-------------------------------------------------------------
      Percentiles      Smallest
 1%            3              0
 5%           22              0
10%           35              0       Obs           1,336,406
25%           92              0       Sum of wgt.   1,336,406

50%          298                      Mean             788.99
                        Largest       Std. dev.      1407.459
75%          766          15609
90%         2056          15691       Variance        1980940
95%         3518          15704       Skewness       3.798454
99%         7274          15706       Kurtosis       21.81616
r; t=1.19 14:49:22

.                         sum durretrisk1 if Emp_Lig1==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
 durretrisk1 |    175,152     1988.07    2497.463          0      15706
r; t=0.22 14:49:22

.                         sum durretrisk1 if Emp_Lig1==1 & (end1==1 & pretrans==1)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
 durretrisk1 |      9,363    3549.416     2831.45          1      15492
r; t=0.58 14:49:23

.                         sum durretrisk1 if Emp_Lig1==1 & (end1==1 & pretrans==6)        

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
 durretrisk1 |     14,708     2451.08    2433.645          1      14610
r; t=0.62 14:49:24

.                         sum durretrisk1 if Emp_Lig1==1 & end1==0                

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
 durretrisk1 |     63,731    2772.291    3033.404          0      15706
r; t=0.33 14:49:24

.                         
.                 * for delta and lambda: Time in employment in lignite or non lignite, excluding ATZ                     
.                         cap drop durrisk1
r; t=0.00 14:49:24

.                         gen durrisk1=dur
r; t=0.05 14:49:24

.                         replace durrisk1=0 if person103==1 & begepi>=enter103                           
(5,725 real changes made)
r; t=0.06 14:49:24

.                         replace durrisk1=(enter103-begepi)+1 if person103==1 & enter103>begepi & enter103<endepi                        
(2,077 real changes made)
r; t=0.07 14:49:24

.                         
.                 * for delta: Time in employment in lignite/non lignite excluding ATZ
.                         di "check durrisk for deltalig - Time in employment in lignite "
check durrisk for deltalig - Time in employment in lignite 
r; t=0.00 14:49:24

.                         sum durrisk1 if Emp_Lig1==1                     

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    durrisk1 |    175,152    1971.429    2484.101          0      15706
r; t=0.25 14:49:24

.                         sum durrisk1 if Emp_Lig1==1 & (end1==1 & pretrans==7) 

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    durrisk1 |     22,273    893.1982    995.9638          1      14154
r; t=0.60 14:49:25

.                         sum durrisk1 if Emp_Lig1==1 & (end1==1 & pretrans==8)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    durrisk1 |      3,873    767.9321     1397.37          0      15251
r; t=0.64 14:49:26

.                         sum durrisk1 if Emp_Lig1==1 &  end1==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    durrisk1 |     63,731    2756.493    3039.315          0      15706
r; t=0.37 14:49:26

.                         
.                         di "check durrisk for deltanonlig - Time in employment non lignite"
check durrisk for deltanonlig - Time in employment non lignite
r; t=0.00 14:49:26

.                         sum durrisk1 if Emp_NonLig1==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    durrisk1 |    436,940    915.2989    1378.901          0      14610
r; t=0.28 14:49:26

.                         sum durrisk1 if Emp_NonLig1==1 & (end1==1 & pretrans==11)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    durrisk1 |    171,765    619.6116    972.9204          0      14338
r; t=0.61 14:49:27

.                         sum durrisk1 if Emp_NonLig1==1 & (end1==1 & pretrans==12)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    durrisk1 |      4,493    673.4293    1009.574          1      12744
r; t=0.64 14:49:28

.                         sum durrisk1 if Emp_NonLig1==1 &  end1==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    durrisk1 |    112,577    1463.595    1904.346          0      14610
r; t=0.37 14:49:28

.                         
.                 * for lambda: Time in unemployment after lignite/non lignite excluding ATZ
.                         di "check durrisk for lambdalig - Time in unemployment after lignite"           
check durrisk for lambdalig - Time in unemployment after lignite
r; t=0.00 14:49:28

.                         sum durrisk1 if Unemp_postLig1==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    durrisk1 |     54,119     781.074    867.8051          0       9406
r; t=0.24 14:49:28

.                         sum durrisk1 if Unemp_postLig1==1 & (end1==1 & posttrans==7)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    durrisk1 |     22,269    556.7989    635.6954          0       7397
r; t=0.61 14:49:29

.                         sum durrisk1 if Unemp_postLig1==1 & (end1==1 & posttrans==8)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    durrisk1 |      3,873    180.9422    246.0353          0       3957
r; t=0.59 14:49:29

.                         sum durrisk1 if Unemp_postLig1==1 &  end1==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    durrisk1 |     14,400    790.0437    1079.709          0       9406
r; t=0.33 14:49:30

.                         
.                         di "check durrisk for lambdalig - Time in unemployment after non lignite"
check durrisk for lambdalig - Time in unemployment after non lignite
r; t=0.00 14:49:30

.                         tab posttrans Unemp_postNonLig1                                                 

                      |   Unemp_postNonLig1
            posttrans |         0          1 |     Total
----------------------+----------------------+----------
Not post trans'n out  |   983,533     52,552 | 1,036,085 
in retirement (no min |       317          0 |       317 
 post-lignite vocatio |       158          0 |       158 
post-lignite normal e |    30,998          0 |    30,998 
post-lignite unemp/AL |    14,709          0 |    14,709 
post-lignite unemp/AL |    22,273          0 |    22,273 
post-lignite unemp/AL |     3,873          0 |     3,873 
post-lignite black ho |    51,735          0 |    51,735 
transition out of NON |        NA    171,763 |   171,765 
transition out of NON |         0      4,493 |     4,493 
----------------------+----------------------+----------
                Total | 1,107,598    228,808 | 1,336,406 
r; t=0.40 14:49:30

.                         sum durrisk1 if Unemp_postNonLig1==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    durrisk1 |    228,808    418.6154    756.9505          0       9994
r; t=0.23 14:49:30

.                         sum durrisk1 if Unemp_postNonLig1==1 & (end1==1 & posttrans==11)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    durrisk1 |    171,763    291.9608    489.9012          0       7716
r; t=0.57 14:49:31

.                         sum durrisk1 if Unemp_postNonLig1==1 & (end1==1 & posttrans==12)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    durrisk1 |      4,493    248.1765    325.8707          1       4748
r; t=0.65 14:49:32

.                         sum durrisk1 if Unemp_postNonLig1==1 &  end1==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
    durrisk1 |     51,487    845.3441    1206.039          0       9994
r; t=0.38 14:49:32

. 
.         save ${data}\temp5.dta, replace
file \\iab.baintern.de\DFS\017\Ablagen\D01700-Projekte\D01700-COAL\data\temp5.dta saved
r; t=26.37 14:49:58

. 
.         * Distribution of short spell
.         tab pretrans durretrisk1 if durretrisk1<7

                      |                                 durretrisk1
             pretrans |         0          1          2          3          4          5          6 |     Total
----------------------+-----------------------------------------------------------------------------+----------
Not pre (observed) tr |     1,939      4,062      3,078      2,778      2,332      2,572      2,217 |    18,978 
pre retirement (witho |         0         NA         NA         NA         NA         NA         NA |        NA 
pre trans'n 2 other n |        NA         NA         NA         NA         NA         NA         NA |        NA 
pre trans'n to unem/A |         0         NA         NA         NA         NA         NA         NA |        NA 
pre trans'n 2 unemp/A |         0         NA         NA         NA         NA         NA         NA |        NA 
pre trans'n 2 unemp/A |        NA         NA         NA         NA         NA         NA         NA |        NA 
pre trans'n 2 black h |        NA        182         NA         NA         NA         NA         NA |       433 
pre transition out of |        NA      1,521      1,037        738        681        871        514 |     5,363 
pre transition out of |         0         NA         NA         NA         NA         NA         NA |        NA 
----------------------+-----------------------------------------------------------------------------+----------
                Total |     1,971      5,849      4,227      3,601      3,100      3,532      2,783 |    25,063 
r; t=0.23 14:49:59

.         tab posttrans durretrisk1 if durretrisk1<7

                      |                                 durretrisk1
            posttrans |         0          1          2          3          4          5          6 |     Total
----------------------+-----------------------------------------------------------------------------+----------
Not post trans'n out  |     1,451      4,424      2,768      2,372      1,953      2,360      1,679 |    17,007 
in retirement (no min |       230         NA         NA          0          0         NA         NA |       235 
 post-lignite vocatio |        NA         NA         NA         NA          0         NA          0 |        NA 
post-lignite normal e |        NA         NA         NA         NA         NA         NA         NA |        NA 
post-lignite unemp/AL |        NA         NA         NA         NA          0          0         NA |        NA 
post-lignite unemp/AL |        NA         NA         NA        110         NA         NA         NA |       452 
post-lignite unemp/AL |        NA        118         NA        154         NA         NA         NA |       404 
post-lignite black ho |       231         NA         NA          0          0          0          0 |       232 
transition out of NON |        NA      1,224      1,357        937        991      1,059        966 |     6,543 
transition out of NON |        NA         NA         NA         NA         NA         NA         NA |       103 
----------------------+-----------------------------------------------------------------------------+----------
                Total |     1,971      5,849      4,227      3,601      3,100      3,532      2,783 |    25,063 
r; t=0.23 14:49:59

. 
.         tab pretrans durrisk1 if durrisk1<7

                      |                                   durrisk1
             pretrans |         0          1          2          3          4          5          6 |     Total
----------------------+-----------------------------------------------------------------------------+----------
Not pre (observed) tr |     5,517      4,059      3,075      2,777      2,329      2,572      2,216 |    22,545 
pre retirement (witho |        NA         NA         NA         NA         NA         NA         NA |       152 
pre trans'n 2 vocatio |        NA          0          0          0          0          0          0 |        NA 
pre trans'n 2 other n |        NA         NA         NA         NA         NA         NA         NA |        NA 
pre trans'n to unem/A |         0         NA         NA         NA         NA         NA         NA |        NA 
pre trans'n 2 unemp/A |         0         NA         NA         NA         NA         NA         NA |        NA 
pre trans'n 2 unemp/A |        NA         NA         NA         NA         NA         NA         NA |        NA 
pre trans'n 2 black h |        NA        182         NA         NA         NA         NA         NA |       459 
pre transition out of |        NA      1,521      1,037        738        681        871        514 |     5,364 
pre transition out of |         0         NA         NA         NA         NA         NA         NA |        NA 
----------------------+-----------------------------------------------------------------------------+----------
                Total |     5,725      5,846      4,217      3,600      3,097      3,533      2,783 |    28,801 
r; t=0.24 14:49:59

.         tab posttrans durrisk1 if durrisk1<7

                      |                                   durrisk1
            posttrans |         0          1          2          3          4          5          6 |     Total
----------------------+-----------------------------------------------------------------------------+----------
Not post trans'n out  |     4,890      4,423      2,759      2,371      1,950      2,361      1,680 |    20,434 
in retirement (no min |       231         NA         NA          0          0         NA         NA |       236 
 post-lignite vocatio |        NA         NA         NA         NA          0         NA          0 |        NA 
post-lignite normal e |       183         NA         NA         NA         NA         NA         NA |       224 
post-lignite unemp/AL |        NA          0         NA         NA          0          0         NA |        NA 
post-lignite unemp/AL |        NA         NA         NA        110         NA         NA         NA |       452 
post-lignite unemp/AL |        NA        117         NA        154         NA         NA         NA |       408 
post-lignite black ho |       377          0          0          0          0          0          0 |       377 
transition out of NON |        NA      1,223      1,357        937        991      1,059        966 |     6,548 
transition out of NON |         0         NA         NA         NA         NA         NA         NA |       103 
----------------------+-----------------------------------------------------------------------------+----------
                Total |     5,725      5,846      4,217      3,600      3,097      3,533      2,783 |    28,801 
r; t=0.24 14:49:59

. 
.         /* Different possibilities of scenarios in the loop to test excluding outliers
>                 - 0 : use whole sample
>                 - 0.01 : exclude bottom and top outliers of the duration distribution at percentile 0.01
>                 - 1 : exclude bottom and top outliers of the duration distribution at percentile 1
>                 - 2 : exclude bottom and top outliers of the duration distribution at percentile 2
>                 - 3 : excluding 1 day duration spells
>                 - 4 : excluding 1 to 6 days duration spells
>                 - 5 : excluding 1% top duration spells
>                 - 6 : excluding 2% top duration spells
>                 - 7 : excluding all observations of people who have at least 1 day spell
>         */
.         
.         foreach x in 0 /*0.01 0.1 1 2 3 4 5 6 7*/ {
  2.         
.         use ${data}\temp5.dta, clear
  3.         
.         cap drop keep
  4.         gen keep=1
  5.         
.         * Whole sample
.         if  `x'==0 {
  6.         di "Analysis on the whole sample"
  7.         }
  8.                 
.         * Removing spells at top and bottom percentile of distribution  
.         if `x'>0 & `x'<3 {
  9.         di "Analysis excluding bottom and top outliers of the duration distribution at the p`x'"        
 10.         
.         local top= 100-`x'
 11.         _pctile dur, p(`x' `top')
 12.         return list
 13.         local threshold_bottom = `r(r1)'        
 14.         local threshold_top = `r(r2)'   
 15.         replace keep=0 if dur<`threshold_bottom' | dur>`threshold_top'
 16.         }
 17.         
.         * Removing 1 day duration spells
.         if `x'==3 {
 18.         di "Analysis excluding 1 day duration spells"
 19.         replace keep=0 if dur==1
 20.         }       
 21.         
.         * Removing  1 to 6 day duration spells
.         if `x'==4 {
 22.         di "Analysis excluding 1 to 6 day duration spells"
 23.         replace keep=0 if dur<7
 24.         }       
 25.         
.         * Removing 1% top distribution spells
.         if `x'==5 {
 26.         di "Analysis excluding 1% top distribution spells"
 27.         _pctile dur, p(99)
 28.         return list
 29.         local threshold_top = `r(r1)'   
 30.         replace keep=0 if dur>`threshold_top'
 31.         }
 32.         
.         * Removing 2% top distribution spells
.         if `x'==6 {
 33.         di "Analysis excluding 2% top distribution spells"
 34.         _pctile dur, p(98)
 35.         return list
 36.         local threshold_top = `r(r1)'   
 37.         replace keep=0 if dur>`threshold_top'
 38.         }
 39.         
.         * Removing all observations of people who have at least 1 day spell
.         if `x'==7 {
 40.         di "Analysis excluding all observations of people who have at least 1 day spell"
 41.         cap drop shortdur
 42.         gen shortdur=0
 43.         bysort persnr (begepi): replace shortdur=1 if dur==1
 44.         tab shortdur
 45.         *copy values to all observations
.         cap drop indivshort
 46.         bysort persnr (begepi): egen indivshort=max(shortdur)
 47.         tab indivshort
 48.         replace keep=0 if indivshort==1
 49.         }
 50.         
.         tab keep
 51.         sum dur if keep==0, detail
 52.         tab pretrans keep
 53.         tab posttrans keep
 54.         drop if keep==0 
 55.         sum dur, detail
 56.         sum durretrisk1, detail
 57.         sum durrisk1, detail            
 58.         
.                 *  (5.2.a)  Retirement probability rho 
.                 di "Lignite leavers' Retirement probability"
 59.                         * Number of distinct persons in cells at risk (working in lignite)
.                         cap drop dummy infopers countinfopers
 60.                         gen dummy=0
 61.                         replace dummy=1 if Emp_Lig1==1 & durretrisk1!=0
 62.                         bys persnr: egen infopers=max(dummy)
 63.                         bys persnr: gen countinfopers=1 if (infopers==1 & _n==1)
 64.                         capture noisily estpost sum countinfopers       
 65.                         if _rc!=2000{
 66.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("rhodirect") modify
 67.                                 putexcel A2=("All sample") D1=("nb distinct persons at risk") D2=matrix(e(count))
 68.                                 `putexcelclose'
 69.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("rhoindirect") modify
 70.                                 putexcel A2=("All sample") D1=("nb distinct persons at risk") D2=matrix(e(count))
 71.                                 `putexcelclose'
 72.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("rho") modify
 73.                                 putexcel A2=("All sample") D1=("nb distinct persons at risk") D2=matrix(e(count))
 74.                                 `putexcelclose'
 75.                         }
 76.                         * Exposure time : time of employment in lignite (Emp_Lig1==1)
.                         capture noisily estpost sum durretrisk1 if Emp_Lig1==1
 77.                         if _rc!=2000{
 78.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("rhodirect") modify
 79.                                 putexcel E1=("time at risk") E2=matrix(e(sum))
 80.                                 `putexcelclose'
 81.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("rhoindirect") modify
 82.                                 putexcel E1=("time at risk") E2=matrix(e(sum))
 83.                                 `putexcelclose'
 84.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("rho") modify
 85.                                 putexcel E1=("time at risk") E2=matrix(e(sum))
 86.                                 `putexcelclose'
 87.                         }
 88.                         *rhodirect (ex mudirect)
.                         * Transitions : not right-censored (end==1) and pretrans==1
.                         capture noisily estpost sum end1 if pretrans==1
 89.                         if _rc!=2000{
 90.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("rhodirect") modify
 91.                                 putexcel C1=("transitions") C2=matrix(e(sum))
 92.                                 `putexcelclose'
 93.                                 if ${iab}==1{
 94.                                         putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("rhodirect") modify
 95.                                         putexcel B1=("rhodirect") B2=formula(=C2/E2)
 96.                                         `putexcelclose'
 97.                                 }
 98.                         }
 99.                         * rhoindirect (ex-mu indirect)
.                         * Transitions : not right-censored (end==1) and pretrans==6
.                         capture noisily estpost sum end1 if pretrans==6
100.                         if _rc!=2000{
101.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("rhoindirect") modify
102.                                 putexcel C1=("transitions") C2=matrix(e(sum))
103.                                 `putexcelclose'
104.                                 if ${iab}==1{
105.                                         putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("rhoindirect") modify
106.                                         putexcel B1=("rhoindirect") B2=formula(=C2/E2)
107.                                         `putexcelclose'
108.                                 }
109.                         }
110.                         *rho (ex-mu)
.                         * Transitions : not right-censored (end==1) and pretrans==1 or 6
.                         capture noisily estpost sum end1 if pretrans==1 | pretrans==6
111.                         if _rc!=2000{
112.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("rho") modify
113.                                 putexcel C1=("transitions") C2=matrix(e(sum))
114.                                 `putexcelclose'
115.                                 if ${iab}==1{
116.                                         putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("rho") modify
117.                                         putexcel B1=("rho") B2=formula(=C2/E2)
118.                                         `putexcelclose'
119.                                 }
120.                         }
121. 
.                 
.                 *  (5.2.b) Job loss rate delta 
.                 
.                 di "Lignite leavers' Job loss rate"
122.                         * Number of distinct persons in cells at risk (working in lignite)
.                         cap drop dummy infopers countinfopers
123.                         gen dummy=0
124.                         replace dummy=1 if Emp_Lig1==1 & durrisk1!=0
125.                         bys persnr: egen infopers=max(dummy)
126.                         bys persnr: gen countinfopers=1 if (infopers==1 & _n==1)
127.                         capture noisily estpost sum countinfopers       
128.                         if _rc!=2000{
129.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("deltalig") modify
130.                                 putexcel A2=("All sample") D1=("nb distinct persons at risk") D2=matrix(e(count))
131.                                 `putexcelclose'
132.                         }
133. 
.                         *delta lig
.                         * Exposure time : time of employment in lignite (Emp_Lig==1)
.                         capture noisily estpost sum durrisk1 if Emp_Lig1==1
134.                         if _rc!=2000{
135.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("deltalig") modify
136.                                 putexcel E1=("time at risk") E2=matrix(e(sum))
137.                                 `putexcelclose'
138.                         }
139.                         * Transitions : not right-censored (end==1) and pretrans==7 or 8
.                         capture noisily estpost sum end1 if pretrans==7 | pretrans==8
140.                         if _rc!=2000{
141.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("deltalig") modify
142.                                 putexcel C1=("transitions") C2=matrix(e(sum))
143.                                 `putexcelclose'
144.                                 if ${iab}==1{
145.                                         putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("deltalig") modify
146.                                         putexcel B1=("deltalig") B2=formula(=C2/E2)
147.                                         `putexcelclose'
148.                                 }
149.                         }
150.                         
.                 di "Non Lignite leavers' Job loss rate" 
151.                         * Number of distinct persons in cells at risk (working in non lignite)
.                         cap drop dummy infopers countinfopers
152.                         gen dummy=0
153.                         replace dummy=1 if Emp_NonLig1==1 & durrisk1!=0
154.                         bys persnr: egen infopers=max(dummy)
155.                         bys persnr: gen countinfopers=1 if (infopers==1 & _n==1)
156.                         capture noisily estpost sum countinfopers
157.                         if _rc!=2000{
158.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("deltanonlig") modify
159.                                 putexcel A2=("All sample") D1=("nb distinct persons at risk") D2=matrix(e(count))
160.                                 `putexcelclose'
161.                         }
162.                 *delta nonlig
.                         * Exposure time : time of employment NOT in lignite (Emp_NoLig==1) 
.                         capture noisily estpost sum durrisk1 if Emp_NonLig1==1
163.                         if _rc!=2000{
164.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("deltanonlig") modify
165.                                 putexcel E1=("time at risk") E2=matrix(e(sum))
166.                                 `putexcelclose'
167.                         }
168.                         * Transitions : not right-censored (end==1) and pretrans==11 or 12
.                         capture noisily estpost sum end1 if pretrans==11 | pretrans==12
169.                         if _rc!=2000{
170.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("deltanonlig") modify
171.                                 putexcel C1=("transitions") C2=matrix(e(sum))
172.                                 `putexcelclose'
173.                                 if ${iab}==1{
174.                                         putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("deltanonlig") modify
175.                                         putexcel B1=("deltanonlig") B2=formula(=C2/E2)
176.                                         `putexcelclose'
177.                                 }
178.                         
.                         }
179.                 
.                 *  (5.2.c) Job offer arrival rate / Job finding rate lambda     
.                 di "Lignite leavers' Job finding rate (ending in a new lignite or non-lignite job)"
180.                         * Number of distinct persons in cells unemployed after lignite
.                                 * - ending in non lignite or lignite employment (end1==1 & (posttrans==7|posttrans==8))  
.                                 * - OR censored (end1==0)
.                         cap drop dummy infopers countinfopers
181.                         gen dummy=0
182.                         replace dummy=1 if Unemp_postLig1==1 & durrisk1!=0 & ((end1==1 & (posttrans==7 | posttrans==8)) | end1==0)
183.                         bys persnr: egen infopers=max(dummy)
184.                         bys persnr: gen countinfopers=1 if (infopers==1 & _n==1)
185.                         capture noisily estpost sum countinfopers               
186.                         if _rc!=2000{
187.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdanonlig") modify
188.                                 putexcel A2=("All sample") D1=("nb distinct persons at risk") D2=matrix(e(count))
189.                                 `putexcelclose'
190.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdalig") modify
191.                                 putexcel A2=("All sample") D1=("nb distinct persons at risk") D2=matrix(e(count))
192.                                 `putexcelclose'
193.                         }
194.         
.                 * Exposure time : time of unemployment after lignite (Unemp_postLig==1) 
.                         * - ending in non lignite or lignite employment (end==1 & (posttrans==7|posttrans==8)) 
.                         * - OR censored (end==0)
.                         capture noisily estpost sum durrisk1 if Unemp_postLig1==1 & ((end1==1 & (posttrans==7 | posttrans==8)) | end1==0)       
195.                         if _rc!=2000{
196.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdanonlig") modify
197.                                 putexcel E1=("time at risk") E2=matrix(e(sum))
198.                                 `putexcelclose'
199.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdalig") modify
200.                                 putexcel E1=("time at risk") E2=matrix(e(sum))
201.                                 `putexcelclose'
202.                         }
203.                 *lambda nonlig
.                         * Transitions : not right-censored (end==1) and posttrans==7(NonLig)
.                         capture noisily estpost sum end1 if posttrans==7
204.                         if _rc!=2000{
205.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdanonlig") modify
206.                                 putexcel C1=("transitions") C2=matrix(e(sum))
207.                                 `putexcelclose'
208.                                 if ${iab}==1{
209.                                         putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdanonlig") modify
210.                                         putexcel B1=("lambdanonlig") B2=formula(=C2/E2)
211.                                         `putexcelclose'
212.                                 }
213.                         }
214.                 *lambda lig
.                         * Transitions : not right-censored (end==1) and posttrans==8(Lig)
.                         capture noisily estpost sum end1 if posttrans==8
215.                         if _rc!=2000{
216.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdalig") modify
217.                                 putexcel C1=("transitions") C2=matrix(e(sum))
218.                                 `putexcelclose'
219.                                 if ${iab}==1{
220.                                         putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdalig") modify
221.                                         putexcel B1=("lambdalig") B2=formula(=C2/E2)
222.                                         `putexcelclose'
223.                                 }
224.                         }
225. 
.         di "Non Lignite leavers' Job finding rate (ending in a non-lignite job)"
226.                         * Number of distinct persons unemployed after non lignite
.                         * - ending in Non lignite or lignite employment (end1==1 & (posttrans==11 | posttrans==12) 
.                         * - OR censored (end1==0)
.                         cap drop dummy infopers countinfopers
227.                         gen dummy=0
228.                         replace dummy=1 if Unemp_postNonLig1==1 & ((end1==1 & (posttrans==11 | posttrans==12)) | end1==0) & durrisk1!=0
229.                         bys persnr: egen infopers=max(dummy)
230.                         bys persnr: gen countinfopers=1 if (infopers==1 & _n==1)
231.                         capture noisily estpost sum countinfopers
232.                         if _rc!=2000{
233.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdazerolig") modify
234.                                 putexcel A2=("All sample") D1=("nb distinct persons at risk") D2=matrix(e(count))
235.                                 `putexcelclose'
236.                         }
237.                 * Exposure time : time of unemployment after non lignite (Unemp_postnonLig==1) 
.                         * - ending in Non lignite or lignite employment (end==1 & (posttrans==11 | posttrans==12) 
.                         * - OR censored (end==0)
.                 capture noisily estpost sum durrisk1 if Unemp_postNonLig1==1 & ((end1==1 & (posttrans==11 | posttrans==12)) | end1==0)  
238.                 if _rc!=2000{
239.                         putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdazerolig") modify
240.                         putexcel E1=("time at risk") E2=matrix(e(sum))
241.                         `putexcelclose'
242.                 }
243.                 * Transitions : not right-censored (end==1) and posttrans==11 (NonLig)
.                 capture noisily estpost sum end1 if posttrans==11
244.                 if _rc!=2000{
245.                         putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdazerolig") modify
246.                         putexcel C1=("transitions") C2=matrix(e(sum))
247.                         `putexcelclose'
248.                                 if ${iab}==1{
249.                                         putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdazerolig") modify
250.                                         putexcel B1=("lambdazerolig") B2=formula(=C2/E2)
251.                                         `putexcelclose'
252.                                 }
253.                 }
254.         }       
Analysis on the whole sample

       keep |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |  1,336,406      100.00      100.00
------------+-----------------------------------
      Total |  1,336,406      100.00

                duration in labour mkt status
-------------------------------------------------------------
no observations

                      |    keep
             pretrans |         1 |     Total
----------------------+-----------+----------
Not pre (observed) tr | 1,033,774 | 1,033,774 
pre retirement (witho |     9,443 |     9,443 
pre trans'n 2 vocatio |       159 |       159 
pre trans'n 2 other n |    24,182 |    24,182 
pre trans'n to unem/A |    14,709 |    14,709 
pre trans'n 2 unemp/A |    22,273 |    22,273 
pre trans'n 2 unemp/A |     3,873 |     3,873 
pre trans'n 2 black h |    51,735 |    51,735 
pre transition out of |   171,765 |   171,765 
pre transition out of |     4,493 |     4,493 
----------------------+-----------+----------
                Total | 1,336,406 | 1,336,406 

                      |    keep
            posttrans |         1 |     Total
----------------------+-----------+----------
Not post trans'n out  | 1,036,085 | 1,036,085 
in retirement (no min |       317 |       317 
 post-lignite vocatio |       158 |       158 
post-lignite normal e |    30,998 |    30,998 
post-lignite unemp/AL |    14,709 |    14,709 
post-lignite unemp/AL |    22,273 |    22,273 
post-lignite unemp/AL |     3,873 |     3,873 
post-lignite black ho |    51,735 |    51,735 
transition out of NON |   171,765 |   171,765 
transition out of NON |     4,493 |     4,493 
----------------------+-----------+----------
                Total | 1,336,406 | 1,336,406 
(0 observations deleted)

                duration in labour mkt status
-------------------------------------------------------------
      Percentiles      Smallest
 1%            3              1
 5%           23              1
10%           35              1       Obs           1,336,406
25%           92              1       Sum of wgt.   1,336,406

50%          300                      Mean           792.4765
                        Largest       Std. dev.      1414.145
75%          771          15609
90%         2070          15691       Variance        1999805
95%         3528          15704       Skewness       3.806334
99%         7305          15706       Kurtosis       21.91888

                         durretrisk1
-------------------------------------------------------------
      Percentiles      Smallest
 1%            3              0
 5%           22              0
10%           35              0       Obs           1,336,406
25%           92              0       Sum of wgt.   1,336,406

50%          298                      Mean             788.99
                        Largest       Std. dev.      1407.459
75%          766          15609
90%         2056          15691       Variance        1980940
95%         3518          15704       Skewness       3.798454
99%         7274          15706       Kurtosis       21.81616

                          durrisk1
-------------------------------------------------------------
      Percentiles      Smallest
 1%            2              0
 5%           20              0
10%           33              0       Obs           1,336,406
25%           92              0       Sum of wgt.   1,336,406

50%          294                      Mean           785.7497
                        Largest       Std. dev.      1402.736
75%          761          15609
90%         2053          15691       Variance        1967667
95%         3503          15704       Skewness       3.792695
99%         7205          15706       Kurtosis       21.77306
Lignite leavers' Retirement probability
(174,922 real changes made)
(1,224,592 missing values generated)

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |    111814     111814          1          0          0          1          1     111814 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
 durretrisk1 |    175152     175152    1988.07    6237320   2497.463          0      15706   3.48e+08 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
        end1 |      9443       9443   .9996823   .0003176   .0178221          0          1       9440 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
        end1 |     14709      14709    .999932    .000068   .0082453          0          1      14708 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
        end1 |     24152      24152   .9998344   .0001656   .0128685          0          1      24148 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
Lignite leavers' Job loss rate
(173,407 real changes made)
(1,224,601 missing values generated)

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |    111805     111805          1          0          0          1          1     111805 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    durrisk1 |    175152     175152   1971.429    6170758   2484.101          0      15706   3.45e+08 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
        end1 |     26146      26146          1          0          0          1          1      26146 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
Non Lignite leavers' Job loss rate
(434,627 real changes made)
(1,243,880 missing values generated)

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |     92526      92526          1          0          0          1          1      92526 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    durrisk1 |    436940     436940   915.2989    1901367   1378.901          0      14610   4.00e+08 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
        end1 |    176258     176258          1          0          0          1          1     176258 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
Lignite leavers' Job finding rate (ending in a new lignite or non-lignite job)
(40,463 real changes made)
(1,298,810 missing values generated)

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |     37596      37596          1          0          0          1          1      37596 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    durrisk1 |     40542      40542   603.7387   672411.5    820.007          0       9406   2.45e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
        end1 |     22273      22273          1          0          0          1          1      22273 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
        end1 |      3873       3873          1          0          0          1          1       3873 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
Non Lignite leavers' Job finding rate (ending in a non-lignite job)
(227,571 real changes made)
(1,269,408 missing values generated)

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |     66998      66998          1          0          0          1          1      66998 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    durrisk1 |    227743     227743   416.2031   565765.5   752.1738          0       9994   9.48e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
        end1 |    171765     171765          1          0          0          1          1     171765 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
r; t=53.28 14:50:53

. 
.         
.                 
. /* ------------------------------------------------------------------------ */
.  *  (6) To create cells : cut spells crossing macro periods
. /* ------------------------------------------------------------------------ */                  
.                 * bad macro conditions = 1 / good macro conditions = 2
.                 * approximations here
.                 *  good macro is less than 10% unemployment:
.                 *               Lausitz:    from (incl) 2015-
.                 *               Rheinisches from (incl) 2007 -
.                 *       Helmstedter from (incl) 2008 - 
.                 *               Mitteldeutsches from (incl) 2016 -
.                 *               West-Germany: Unemployment below 10% in all years since 1990
.                 *               East Germany: Unemployment below 10% for women since 2012, for men since 2015 
.                 
.                 use ${data}\postcoll.dta, clear
r; t=17.77 14:51:10

. 
.         /* ------------------------------------------------------------- */
.                 * 6.1 Identify change in macro labour market conditions and cut spells crossing macro periods* 
.         /* ------------------------------------------------------------- */     
.         tab mining_area, missing

       mining_area |      Freq.     Percent        Cum.
-------------------+-----------------------------------
  Lausitzer Revier |    247,364       18.51       18.51
  Mitteldt. Revier |    173,031       12.95       31.46
Helmstedter Revier |     21,896        1.64       33.10
Rheinisches Revier |    154,643       11.57       44.67
     Other Reviere |     38,673        2.89       47.56
                 . |    700,799       52.44      100.00
-------------------+-----------------------------------
             Total |  1,336,406      100.00
r; t=0.24 14:51:11

.         
.         cap drop date_macro
r; t=0.00 14:51:11

.         g date_macro=mdy(01,01,1900)
r; t=0.05 14:51:11

.         format date_macro %tdD_m_CY     
r; t=0.00 14:51:11

.         *Lausitz 2015
.         replace date_macro=mdy(01,01,2015) if mining_area==1
(247,364 real changes made)
r; t=0.05 14:51:11

.         *Mitteldeutsches 2016
.         replace date_macro=mdy(01,01,2016) if mining_area==2
(173,031 real changes made)
r; t=0.04 14:51:11

.         *Helmstedter from 2008
.         replace date_macro=mdy(01,01,2008) if mining_area==3    
(21,896 real changes made)
r; t=0.04 14:51:11

.         *Rheinisches from 2007  
.         replace date_macro=mdy(01,01,2007) if mining_area==4    
(154,643 real changes made)
r; t=0.04 14:51:11

.         *East Germany 2012 for women, 2015 for men
.         replace date_macro=mdy(01,01,2012) if (mining_area==. | mining_area==5) & ao_bula>=12 & frau==1
(25,242 real changes made)
r; t=0.08 14:51:11

.         replace date_macro=mdy(01,01,2015) if (mining_area==. | mining_area==5) & ao_bula>=12 & frau==0
(73,656 real changes made)
r; t=0.08 14:51:11

.         
.         tab date_macro, m

 date_macro |      Freq.     Percent        Cum.
------------+-----------------------------------
  01jan1900 |    640,574       47.93       47.93
  01jan2007 |    154,643       11.57       59.50
  01jan2008 |     21,896        1.64       61.14
  01jan2012 |     25,242        1.89       63.03
  01jan2015 |    321,020       24.02       87.05
  01jan2016 |    173,031       12.95      100.00
------------+-----------------------------------
      Total |  1,336,406      100.00
r; t=0.25 14:51:11

. 
.         * JAERE request: Alternative to macro conditions, include only data post 2000
.         *                To implement this sample selection, use the macro variable &
.         *                                cell-based estimation
.                                          
.         if ${sample}==7{
.         replace date_macro=mdy(01,01,2000)
r; t=0.00 14:51:11
. }       
r; t=0.00 14:51:11

.         if (${sample}==8 | ${sample}==9){
.         * JAERE: include spells from all time periods 
.         *  in analysis cut by occupations & regions.
.         replace date_macro=mdy(01,01,1975)
r; t=0.00 14:51:11
.         }
r; t=0.01 14:51:11

.         
.         *Identify when there's a change in macro during one spell
.         cap drop cross
r; t=0.00 14:51:11

.         gen cross=0
r; t=0.03 14:51:11

.         replace cross=1 if date_macro>begepi & date_macro<endepi
(45,982 real changes made)
r; t=0.05 14:51:11

.         tab cross

      cross |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  1,290,424       96.56       96.56
          1 |     45,982        3.44      100.00
------------+-----------------------------------
      Total |  1,336,406      100.00
r; t=0.24 14:51:12

. 
.         *Duplicate the spell
.         cap drop id
r; t=0.00 14:51:12

.         generate long id = _n
r; t=0.05 14:51:12

.         expand 2 if cross==1
(45,982 observations created)
r; t=0.29 14:51:12

.         
.         *Store initial end and beggining of spells
.         g begepi_init=begepi
r; t=0.04 14:51:12

.         g endepi_init=endepi
r; t=0.05 14:51:12

.         
.         g censor=0
r; t=0.03 14:51:12

.         * in first observation
.                 *Replace endepi by date of change 
.                 by id, sort: replace endepi = cond(_n == 1 & cross==1, date_macro-1, endepi) 
(45,982 real changes made)
r; t=3.05 14:51:15

.                 *Create indicator for right censoring
.                 by id, sort: replace censor = cond(_n == 1 & cross==1, 1, censor) 
(45982 real changes made)
r; t=0.09 14:51:15

.         * in second observation
.                 *Replace begepi by date of change       
.                 by id, sort: replace begepi = cond(_n == 2 & cross==1, date_macro, begepi) 
(45,982 real changes made)
r; t=2.43 14:51:18

.                 *Create indicator for left censoring
.                 by id, sort: replace censor = cond(_n == 2 & cross==1, 2, censor)       
(45982 real changes made)
r; t=0.09 14:51:18

.         
.         tab censor

     censor |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  1,290,424       93.35       93.35
          1 |     45,982        3.33       96.67
          2 |     45,982        3.33      100.00
------------+-----------------------------------
      Total |  1,382,388      100.00
r; t=0.22 14:51:18

. 
.         replace dur = (endepi - begepi) + 1
(91,964 real changes made)
r; t=0.06 14:51:18

. 
.         * results:
.         * 1 - dataset with no spells crossing macro periods
.         * 2 - indicator indicating censoring (right=1, left=2, no censoring=0)  
.         
.         * Identify right censoring due to cutting spells
.         * create variable "end_cut" =0 if if right-censored, =1 if spell ending (=failure) is observed.
.         g end_cut=1
r; t=0.05 14:51:18

.         replace end_cut=0 if censor==1
(45,982 real changes made)
r; t=0.03 14:51:18

.         label var end_cut "0 means spell is censored by end of macro conditions"
r; t=0.00 14:51:18

.         tab end_cut

    0 means |
   spell is |
censored by |
     end of |
      macro |
 conditions |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     45,982        3.33        3.33
          1 |  1,336,406       96.67      100.00
------------+-----------------------------------
      Total |  1,382,388      100.00
r; t=0.24 14:51:18

.         tab pretrans end_cut

                      |   0 means spell is
                      |  censored by end of
                      |   macro conditions
             pretrans |         0          1 |     Total
----------------------+----------------------+----------
Not pre (observed) tr |    38,749  1,033,774 | 1,072,523 
pre retirement (witho |     1,115      9,443 |    10,558 
pre trans'n 2 vocatio |        NA        159 |       163 
pre trans'n 2 other n |       906     24,182 |    25,088 
pre trans'n to unem/A |        NA     14,709 |    14,760 
pre trans'n 2 unemp/A |        NA     22,273 |    22,349 
pre trans'n 2 unemp/A |        NA      3,873 |     3,935 
pre trans'n 2 black h |     3,091     51,735 |    54,826 
pre transition out of |     1,908    171,765 |   173,673 
pre transition out of |        NA      4,493 |     4,513 
----------------------+----------------------+----------
                Total |    45,982  1,336,406 | 1,382,388 
r; t=0.42 14:51:19

.         tab posttrans end_cut

                      |   0 means spell is
                      |  censored by end of
                      |   macro conditions
            posttrans |         0          1 |     Total
----------------------+----------------------+----------
Not post trans'n out  |    43,687  1,036,085 | 1,079,772 
in retirement (no min |        NA        317 |       349 
 post-lignite vocatio |         0        158 |       158 
post-lignite normal e |     1,970     30,998 |    32,968 
post-lignite unemp/AL |        NA     14,709 |    14,711 
post-lignite unemp/AL |         0     22,273 |    22,273 
post-lignite unemp/AL |        NA      3,873 |     3,875 
post-lignite black ho |       152     51,735 |    51,887 
transition out of NON |       136    171,765 |   171,901 
transition out of NON |        NA      4,493 |     4,494 
----------------------+----------------------+----------
                Total |    45,982  1,336,406 | 1,382,388 
r; t=0.43 14:51:19

.         
.         * Identify general right censoring due to no observation of transition
.         /* 3 cases
>                 - right censoring created by cutting spell due to macroeconomic change
>                 - right censoring due to last spell
>                 - right censoring due to black hole
>         */
. 
.         *Identify last spell
.         cap drop last_spell
r; t=0.17 14:51:19

.         gen last_spell=0
r; t=0.05 14:51:20

.         by pid (begepi), sort: replace last_spell = (_n == _N)
(146916 real changes made)
r; t=0.44 14:51:20

.         tab pretrans last_spell 

                      |      last_spell
             pretrans |         0          1 |     Total
----------------------+----------------------+----------
Not pre (observed) tr |   934,736    137,787 | 1,072,523 
pre retirement (witho |     1,433      9,125 |    10,558 
pre trans'n 2 vocatio |       163          0 |       163 
pre trans'n 2 other n |    25,086         NA |    25,088 
pre trans'n to unem/A |    14,760          0 |    14,760 
pre trans'n 2 unemp/A |    22,349          0 |    22,349 
pre trans'n 2 unemp/A |     3,935          0 |     3,935 
pre trans'n 2 black h |    54,824         NA |    54,826 
pre transition out of |   173,673          0 |   173,673 
pre transition out of |     4,513          0 |     4,513 
----------------------+----------------------+----------
                Total | 1,235,472    146,916 | 1,382,388 
r; t=0.40 14:51:20

.         tab posttrans last_spell

                      |      last_spell
            posttrans |         0          1 |     Total
----------------------+----------------------+----------
Not post trans'n out  |   949,346    130,426 | 1,079,772 
in retirement (no min |        NA        258 |       349 
 post-lignite vocatio |       154         NA |       158 
post-lignite normal e |    29,940      3,028 |    32,968 
post-lignite unemp/AL |     1,511     13,200 |    14,711 
post-lignite unemp/AL |    22,273          0 |    22,273 
post-lignite unemp/AL |     3,875          0 |     3,875 
post-lignite black ho |    51,887          0 |    51,887 
transition out of NON |   171,901          0 |   171,901 
transition out of NON |     4,494          0 |     4,494 
----------------------+----------------------+----------
                Total | 1,235,472    146,916 | 1,382,388 
r; t=0.41 14:51:21

.         *caution: transition to retirement are not last spell
.         replace last_spell=0 if (posttrans==1 | posttrans==6 | pretrans==1 | pretrans==6)
(22,576 real changes made)
r; t=0.06 14:51:21

.         tab last_spell          

 last_spell |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  1,258,048       91.01       91.01
          1 |    124,340        8.99      100.00
------------+-----------------------------------
      Total |  1,382,388      100.00
r; t=0.26 14:51:21

.         tab pretrans last_spell

                      |      last_spell
             pretrans |         0          1 |     Total
----------------------+----------------------+----------
Not pre (observed) tr |   948,187    124,336 | 1,072,523 
pre retirement (witho |    10,558          0 |    10,558 
pre trans'n 2 vocatio |       163          0 |       163 
pre trans'n 2 other n |    25,086         NA |    25,088 
pre trans'n to unem/A |    14,760          0 |    14,760 
pre trans'n 2 unemp/A |    22,349          0 |    22,349 
pre trans'n 2 unemp/A |     3,935          0 |     3,935 
pre trans'n 2 black h |    54,824         NA |    54,826 
pre transition out of |   173,673          0 |   173,673 
pre transition out of |     4,513          0 |     4,513 
----------------------+----------------------+----------
                Total | 1,258,048    124,340 | 1,382,388 
r; t=0.40 14:51:21

.         tab posttrans last_spell

                      |      last_spell
            posttrans |         0          1 |     Total
----------------------+----------------------+----------
Not post trans'n out  |   958,464    121,308 | 1,079,772 
in retirement (no min |       349          0 |       349 
 post-lignite vocatio |       154         NA |       158 
post-lignite normal e |    29,940      3,028 |    32,968 
post-lignite unemp/AL |    14,711          0 |    14,711 
post-lignite unemp/AL |    22,273          0 |    22,273 
post-lignite unemp/AL |     3,875          0 |     3,875 
post-lignite black ho |    51,887          0 |    51,887 
transition out of NON |   171,901          0 |   171,901 
transition out of NON |     4,494          0 |     4,494 
----------------------+----------------------+----------
                Total | 1,258,048    124,340 | 1,382,388 
r; t=0.41 14:51:22

. 
.         * Identify black holes that come next
.         cap drop black_hole_next
r; t=0.00 14:51:22

.         gen black_hole_next=0
r; t=0.04 14:51:22

.         by pid (begepi), sort: replace black_hole_next=1 if (pid[_n+1]==pid & status[_n+1]==10)
(209060 real changes made)
r; t=0.09 14:51:22

.         tab black_hole_next

black_hole_ |
       next |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |  1,173,328       84.88       84.88
          1 |    209,060       15.12      100.00
------------+-----------------------------------
      Total |  1,382,388      100.00
r; t=0.23 14:51:22

.         tab pretrans black_hole_next

                      |    black_hole_next
             pretrans |         0          1 |     Total
----------------------+----------------------+----------
Not pre (observed) tr |   915,198    157,325 | 1,072,523 
pre retirement (witho |    10,553         NA |    10,558 
pre trans'n 2 vocatio |       163          0 |       163 
pre trans'n 2 other n |    25,086         NA |    25,088 
pre trans'n to unem/A |    14,759         NA |    14,760 
pre trans'n 2 unemp/A |    22,349          0 |    22,349 
pre trans'n 2 unemp/A |     3,935          0 |     3,935 
pre trans'n 2 black h |     3,099     51,727 |    54,826 
pre transition out of |   173,673          0 |   173,673 
pre transition out of |     4,513          0 |     4,513 
----------------------+----------------------+----------
                Total | 1,173,328    209,060 | 1,382,388 
r; t=0.43 14:51:23

.         tab posttrans black_hole_next

                      |    black_hole_next
            posttrans |         0          1 |     Total
----------------------+----------------------+----------
Not post trans'n out  |   878,058    201,714 | 1,079,772 
in retirement (no min |       309         NA |       349 
 post-lignite vocatio |       130         NA |       158 
post-lignite normal e |    27,341      5,627 |    32,968 
post-lignite unemp/AL |    13,213      1,498 |    14,711 
post-lignite unemp/AL |    22,273          0 |    22,273 
post-lignite unemp/AL |     3,875          0 |     3,875 
post-lignite black ho |    51,735        152 |    51,887 
transition out of NON |   171,900         NA |   171,901 
transition out of NON |     4,494          0 |     4,494 
----------------------+----------------------+----------
                Total | 1,173,328    209,060 | 1,382,388 
r; t=0.42 14:51:23

. 
.         tab pretrans end_cut

                      |   0 means spell is
                      |  censored by end of
                      |   macro conditions
             pretrans |         0          1 |     Total
----------------------+----------------------+----------
Not pre (observed) tr |    38,749  1,033,774 | 1,072,523 
pre retirement (witho |     1,115      9,443 |    10,558 
pre trans'n 2 vocatio |        NA        159 |       163 
pre trans'n 2 other n |       906     24,182 |    25,088 
pre trans'n to unem/A |        NA     14,709 |    14,760 
pre trans'n 2 unemp/A |        NA     22,273 |    22,349 
pre trans'n 2 unemp/A |        NA      3,873 |     3,935 
pre trans'n 2 black h |     3,091     51,735 |    54,826 
pre transition out of |     1,908    171,765 |   173,673 
pre transition out of |        NA      4,493 |     4,513 
----------------------+----------------------+----------
                Total |    45,982  1,336,406 | 1,382,388 
r; t=0.41 14:51:24

.         tab posttrans end_cut

                      |   0 means spell is
                      |  censored by end of
                      |   macro conditions
            posttrans |         0          1 |     Total
----------------------+----------------------+----------
Not post trans'n out  |    43,687  1,036,085 | 1,079,772 
in retirement (no min |        NA        317 |       349 
 post-lignite vocatio |         0        158 |       158 
post-lignite normal e |     1,970     30,998 |    32,968 
post-lignite unemp/AL |        NA     14,709 |    14,711 
post-lignite unemp/AL |         0     22,273 |    22,273 
post-lignite unemp/AL |        NA      3,873 |     3,875 
post-lignite black ho |       152     51,735 |    51,887 
transition out of NON |       136    171,765 |   171,901 
transition out of NON |        NA      4,493 |     4,494 
----------------------+----------------------+----------
                Total |    45,982  1,336,406 | 1,382,388 
r; t=0.40 14:51:24

.         
.         * Here generate variable to indicate that there really is a transition. 
.         * end=1 means there is a transition.
.         * Alternatives to a transition are 
.         * (1) censoring due to macro (end_cut=0); (2) last spell in survey; (3) black hole afterwards.
.         * => if none of these three, we have a transition.
.         cap drop end
r; t=0.00 14:51:24

. 
.         g end=1
r; t=0.05 14:51:24

.         replace end=0 if (end_cut==0 | last_spell==1 | black_hole_next==1)
(377,782 real changes made)
r; t=0.05 14:51:24

.         tab end

        end |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |    377,782       27.33       27.33
          1 |  1,004,606       72.67      100.00
------------+-----------------------------------
      Total |  1,382,388      100.00
r; t=0.24 14:51:24

.         tab pretrans end        

                      |          end
             pretrans |         0          1 |     Total
----------------------+----------------------+----------
Not pre (observed) tr |   318,817    753,706 | 1,072,523 
pre retirement (witho |     1,120      9,438 |    10,558 
pre trans'n 2 vocatio |        NA        159 |       163 
pre trans'n 2 other n |       910     24,178 |    25,088 
pre trans'n to unem/A |        NA     14,708 |    14,760 
pre trans'n 2 unemp/A |        NA     22,273 |    22,349 
pre trans'n 2 unemp/A |        NA      3,873 |     3,935 
pre trans'n 2 black h |    54,813         NA |    54,826 
pre transition out of |     1,908    171,765 |   173,673 
pre transition out of |        NA      4,493 |     4,513 
----------------------+----------------------+----------
                Total |   377,782  1,004,606 | 1,382,388 
r; t=0.42 14:51:25

.         tab posttrans end

                      |          end
            posttrans |         0          1 |     Total
----------------------+----------------------+----------
Not post trans'n out  |   365,261    714,511 | 1,079,772 
in retirement (no min |        NA        277 |       349 
 post-lignite vocatio |        NA        126 |       158 
post-lignite normal e |    10,625     22,343 |    32,968 
post-lignite unemp/AL |     1,500     13,211 |    14,711 
post-lignite unemp/AL |         0     22,273 |    22,273 
post-lignite unemp/AL |        NA      3,873 |     3,875 
post-lignite black ho |       152     51,735 |    51,887 
transition out of NON |       137    171,764 |   171,901 
transition out of NON |        NA      4,493 |     4,494 
----------------------+----------------------+----------
                Total |   377,782  1,004,606 | 1,382,388 
r; t=0.41 14:51:25

.         
.                 *************************************************************** 
.                 ********(6.0.a ) Diagnostics on new spell durations ***************                     
.                 *************************************************************** 
.                         
.                         * Distribution of duration of last pre-transition status
.                         sum dur if inlist(pretrans,1,6,7,8,11,12), detail

                duration in labour mkt status
-------------------------------------------------------------
      Percentiles      Smallest
 1%            2              1
 5%           18              1
10%           40              1       Obs             229,788
25%          132              1       Sum of wgt.     229,788

50%          343                      Mean            885.987
                        Largest       Std. dev.      1428.904
75%         1006          12744
90%         2283          12909       Variance        2041767
95%         3653          13027       Skewness       3.238542
99%         7655          13300       Kurtosis       15.76514
r; t=0.87 14:51:26

. 
.                         * to fill in (?) in the following lines: status before transition 
.                         tab pretrans statsimple

                      | 0 - unemployed, margemp or ALMP
                      | / 1 - employed / 2 - vocational
             pretrans |         0          1          2 |     Total
----------------------+---------------------------------+----------
Not pre (observed) tr |   417,126    350,045     95,421 |   862,592 
pre retirement (witho |        NA     10,481          0 |    10,558 
pre trans'n 2 vocatio |         0        163          0 |       163 
pre trans'n 2 other n |         0     25,088          0 |    25,088 
pre trans'n to unem/A |         0     14,760          0 |    14,760 
pre trans'n 2 unemp/A |         0     22,349          0 |    22,349 
pre trans'n 2 unemp/A |         0      3,935          0 |     3,935 
pre trans'n 2 black h |     3,418     49,223      2,185 |    54,826 
pre transition out of |         0    173,673          0 |   173,673 
pre transition out of |         0      4,513          0 |     4,513 
----------------------+---------------------------------+----------
                Total |   420,621    654,230     97,606 | 1,172,457 
r; t=0.38 14:51:26

.         
.                         * (a) transition to retirement
.                         disp("Duration of lignite normalemp/vocational series followed directly or indirectly by retirement")
Duration of lignite normalemp/vocational series followed directly or indirectly by retirement
r; t=0.00 14:51:26

.                         sum dur if (pretrans == 6 | pretrans==1), detail

                duration in labour mkt status
-------------------------------------------------------------
      Percentiles      Smallest
 1%           18              1
 5%           91              1
10%          274              1       Obs              25,318
25%          791              1       Sum of wgt.      25,318

50%         1917                      Mean           2813.574
                        Largest       Std. dev.      2592.662
75%         4169          12053
90%         7152          12053       Variance        6721894
95%         8401          12053       Skewness       1.129034
99%         9862          13300       Kurtosis       3.452445
r; t=0.70 14:51:27

.                         disp("Duration of lignite normalemp/vocational series directly followed by retirement")
Duration of lignite normalemp/vocational series directly followed by retirement
r; t=0.00 14:51:27

.                         sum dur if pretrans == 1, detail

                duration in labour mkt status
-------------------------------------------------------------
      Percentiles      Smallest
 1%            9              1
 5%           32              1
10%          113              1       Obs              10,558
25%          746              1       Sum of wgt.      10,558

50%       3103.5                      Mean           3332.397
                        Largest       Std. dev.      2742.759
75%         5114          12053
90%         7396          12053       Variance        7522726
95%         8401          12053       Skewness       .6512427
99%        10957          13300       Kurtosis        2.70088
r; t=0.57 14:51:28

.                         disp("Duration of lignite normalemp/vocational series followed by unemp/margemp/ALMP and then retirement")
Duration of lignite normalemp/vocational series followed by unemp/margemp/ALMP and then retirement
r; t=0.00 14:51:28

.                         sum dur if pretrans == 6, detail

                duration in labour mkt status
-------------------------------------------------------------
      Percentiles      Smallest
 1%           45              1
 5%          213              1
10%          366              1       Obs              14,760
25%          829              1       Sum of wgt.      14,760

50%         1461                      Mean           2442.454
                        Largest       Std. dev.      2412.307
75%         2590          11950
90%       6939.5          12053       Variance        5819223
95%         8036          12053       Skewness        1.58116
99%         9678          12053       Kurtosis       4.656955
r; t=0.55 14:51:28

.                         
.                         * (b) transition to unemployment
.                         disp("Duration of lignite normalemp/vocational series followed by unemployment")
Duration of lignite normalemp/vocational series followed by unemployment
r; t=0.00 14:51:28

.                         sum dur if (pretrans==7 | pretrans==8), detail

                duration in labour mkt status
-------------------------------------------------------------
      Percentiles      Smallest
 1%           21              1
 5%           92              1
10%          184              1       Obs              26,284
25%          274              1       Sum of wgt.      26,284

50%          517                      Mean           870.9413
                        Largest       Std. dev.      1036.195
75%         1183          11507
90%         1827          11598       Variance        1073700
95%         2600          11598       Skewness       3.728012
99%         5168          11625       Kurtosis       24.59576
r; t=0.74 14:51:29

.                         disp("Duration of lignite normalemp/vocational series followed by unemployment ending in employment in NON lignite")
Duration of lignite normalemp/vocational series followed by unemployment ending in employment in NON lignite
r; t=0.01 14:51:29

.                         sum dur if (pretrans==7), detail

                duration in labour mkt status
-------------------------------------------------------------
      Percentiles      Smallest
 1%           29              1
 5%          150              1
10%          213              1       Obs              22,349
25%          305              1       Sum of wgt.      22,349

50%          547                      Mean           890.5215
                        Largest       Std. dev.      980.3202
75%         1258          10774
90%         1827          11507       Variance       961027.8
95%         2557          11507       Skewness       3.522166
99%         4749          11598       Kurtosis       23.42669
r; t=0.63 14:51:30

.                         disp("Duration of lignite normalemp/vocational series followed by unemployment ending in employment in lignite")
Duration of lignite normalemp/vocational series followed by unemployment ending in employment in lignite
r; t=0.00 14:51:30

.                         sum dur if (pretrans==8), detail

                duration in labour mkt status
-------------------------------------------------------------
      Percentiles      Smallest
 1%            3              1
 5%           31              1
10%           79              1       Obs               3,935
25%          184              1       Sum of wgt.       3,935

50%          352                      Mean           759.7349
                        Largest       Std. dev.      1303.643
75%          608          10957
90%         1827          11507       Variance        1699486
95%         3061          11598       Skewness       4.111336
99%         7555          11625       Kurtosis       23.88291
r; t=0.56 14:51:30

.         
.                         disp("Duration of NON-lignite normalemp/vocational series followed by unemployment")
Duration of NON-lignite normalemp/vocational series followed by unemployment
r; t=0.00 14:51:30

.                         sum dur if (pretrans==11 | pretrans==12), detail        

                duration in labour mkt status
-------------------------------------------------------------
      Percentiles      Smallest
 1%            2              1
 5%           13              1
10%           31              1       Obs             178,186
25%           98              1       Sum of wgt.     178,186

50%          266                      Mean           614.3204
                        Largest       Std. dev.      958.0396
75%          670          12356
90%         1674          12744       Variance       917839.9
95%         2552          12909       Skewness       3.425246
99%         4679          13027       Kurtosis       19.58395
r; t=0.87 14:51:31

.                         disp("Duration NON-lignite normalemp/vocational series followed by unemployment ending in employment in NON lignite")
Duration NON-lignite normalemp/vocational series followed by unemployment ending in employment in NON lignite
r; t=0.00 14:51:31

.                         sum dur if (pretrans==11), detail

                duration in labour mkt status
-------------------------------------------------------------
      Percentiles      Smallest
 1%            2              1
 5%           13              1
10%           31              1       Obs             173,673
25%           98              1       Sum of wgt.     173,673

50%          264                      Mean            612.862
                        Largest       Std. dev.      956.7359
75%          669          11597
90%         1673          11858       Variance       915343.7
95%         2545          12909       Skewness       3.418149
99%         4677          13027       Kurtosis       19.40673
r; t=0.71 14:51:32

.                         disp("Duration of NON-lignite normalemp/vocational series followed by unemployment ending in employment in lignite")
Duration of NON-lignite normalemp/vocational series followed by unemployment ending in employment in lignite
r; t=0.00 14:51:32

.                         sum dur if (pretrans==12), detail

                duration in labour mkt status
-------------------------------------------------------------
      Percentiles      Smallest
 1%            4              1
 5%           21              1
10%           43              1       Obs               4,513
25%          121              1       Sum of wgt.       4,513

50%          306                      Mean           670.4449
                        Largest       Std. dev.      1005.432
75%          731          10773
90%         1827          11081       Variance        1010893
95%         2586          12356       Skewness       3.648826
99%         4718          12744       Kurtosis       24.94573
r; t=0.61 14:51:32

.                         
.                         * (c) transition from unemployment      
.                         disp("Duration of Post lignite unemployment series followed by employment")
Duration of Post lignite unemployment series followed by employment
r; t=0.00 14:51:32

.                         sum dur if (posttrans==7 | posttrans==8), detail

                duration in labour mkt status
-------------------------------------------------------------
      Percentiles      Smallest
 1%            3              1
 5%           14              1
10%           31              1       Obs              26,148
25%          102              1       Sum of wgt.      26,148

50%          304                      Mean           501.1207
                        Largest       Std. dev.       609.005
75%          709           6329
90%         1156           6393       Variance       370887.1
95%         1552           7152       Skewness       3.192973
99%         2922           7397       Kurtosis       20.03473
r; t=0.68 14:51:33

.                         * detailed by sex/year
.                         tabulate frau if (posttrans==7 | posttrans==8), summarize(dur) 

            |  Summary of duration in labour mkt
            |               status
       frau |        Mean   Std. dev.       Freq.
------------+------------------------------------
       mann |   385.55137   471.66099      18,122
       frau |   762.06591    779.6285       8,026
------------+------------------------------------
      Total |   501.12066   609.00498      26,148
r; t=0.98 14:51:34

.                         tabulate jahrend if (posttrans==7 | posttrans==8), summarize(dur) 

            |  Summary of duration in labour mkt
year at end |               status
   of spell |        Mean   Std. dev.       Freq.
------------+------------------------------------
       1976 |   246.70588   142.36606          NA
       1977 |   143.40541   138.93033          NA
       1978 |   118.51429   103.23741          NA
       1979 |   159.73171   245.53635          NA
       1980 |   109.68889   153.20809          NA
       1981 |   131.67797   142.05189          NA
       1982 |   174.06349   239.34934         126
       1983 |   211.32955   146.83244         264
       1984 |   393.92623   272.98101         122
       1985 |   399.39535    456.1661          NA
       1986 |   323.78333    455.4691          NA
       1987 |   343.50943   468.66076          NA
       1988 |   190.63158   199.45754          NA
       1989 |   266.58824   496.73156          NA
       1990 |      335.08   441.06235          NA
       1991 |   188.81818   202.43635          NA
       1992 |   67.859604   80.720602       1,161
       1993 |   168.70311   125.20161       4,665
       1994 |   425.94611   209.90691       3,173
       1995 |   689.34474   316.68276       2,280
       1996 |   573.77426   485.53652       2,494
       1997 |   567.96861    470.7674       2,166
       1998 |   677.16567   565.20548       1,998
       1999 |   654.47558   593.87291       1,556
       2000 |   651.83065   710.92464       1,305
       2001 |   679.76399   740.41329         822
       2002 |   782.96917   798.53536         519
       2003 |   639.09037   852.52805         509
       2004 |   889.76886   1051.2857         411
       2005 |   755.85879    1021.369         347
       2006 |   804.00338   1119.8606         296
       2007 |   671.03984   1062.9795         251
       2008 |   883.80702   1375.5936         228
       2009 |   1058.5944   1673.0165         143
       2010 |   1208.7885    1921.968         156
       2011 |   857.84615    1575.677         104
       2012 |   368.82143   1038.6873          NA
       2013 |   368.40278   766.11492          NA
       2014 |   461.63768   1059.8275          NA
       2015 |   947.67188   1883.8501          NA
       2016 |   358.15873   937.97954          NA
       2017 |   351.33846   693.93996          NA
------------+------------------------------------
      Total |   501.12066   609.00498      26,148
r; t=1.10 14:51:35

.                         tabulate jahrend frau if (posttrans==7 | posttrans==8), summarize(dur) 

                Means, Standard Deviations and Frequencies
                     of duration in labour mkt status

   year at |
    end of |        frau
     spell |      mann       frau |     Total
-----------+----------------------+----------
      1976 | 247.06667        244 | 246.70588
           | 145.03816  172.53405 | 142.36606
           |        NA         NA |        NA
-----------+----------------------+----------
      1977 | 160.03571  91.666667 | 143.40541
           | 154.51561  47.953102 | 138.93033
           |        NA         NA |        NA
-----------+----------------------+----------
      1978 |       169  88.681818 | 118.51429
           | 157.92614   22.22051 | 103.23741
           |        NA         NA |        NA
-----------+----------------------+----------
      1979 | 206.82143  58.307692 | 159.73171
           | 279.51679  94.077437 | 245.53635
           |        NA         NA |        NA
-----------+----------------------+----------
      1980 | 124.32353  64.454545 | 109.68889
           | 167.96928   84.82849 | 153.20809
           |        NA         NA |        NA
-----------+----------------------+----------
      1981 | 138.46809  105.08333 | 131.67797
           | 138.34218  159.35635 | 142.05189
           |        NA         NA |        NA
-----------+----------------------+----------
      1982 | 187.48148  93.555556 | 174.06349
           | 254.73756  69.256745 | 239.34934
           |       108         NA |       126
-----------+----------------------+----------
      1983 | 216.95082     142.75 | 211.32955
           |  147.0229   128.9271 | 146.83244
           |       244         NA |       264
-----------+----------------------+----------
      1984 | 427.54545      85.75 | 393.92623
           | 265.24832  87.322938 | 272.98101
           |       110         NA |       122
-----------+----------------------+----------
      1985 | 504.25397  112.17391 | 399.39535
           | 476.95459  212.47559 |  456.1661
           |        NA         NA |        NA
-----------+----------------------+----------
      1986 | 444.26316  115.68182 | 323.78333
           | 532.39199   108.6775 |  455.4691
           |        NA         NA |        NA
-----------+----------------------+----------
      1987 | 442.06061      180.9 | 343.50943
           | 559.03376  173.43098 | 468.66076
           |        NA         NA |        NA
-----------+----------------------+----------
      1988 | 251.65714  93.545455 | 190.63158
           | 232.29367  51.410326 | 199.45754
           |        NA         NA |        NA
-----------+----------------------+----------
      1989 |  350.3125  125.57895 | 266.58824
           | 609.48939  110.35665 | 496.73156
           |        NA         NA |        NA
-----------+----------------------+----------
      1990 | 487.42308  170.04167 |    335.08
           | 560.29558  136.23237 | 441.06235
           |        NA         NA |        NA
-----------+----------------------+----------
      1991 |    242.12  118.68421 | 188.81818
           | 244.82397  94.028986 | 202.43635
           |        NA         NA |        NA
-----------+----------------------+----------
      1992 | 62.972445  82.537931 | 67.859604
           | 84.825943  64.840184 | 80.720602
           |       871        290 |      1161
-----------+----------------------+----------
      1993 | 144.62947  238.45029 | 168.70311
           | 111.80996  135.42583 | 125.20161
           |      3468       1197 |      4665
-----------+----------------------+----------
      1994 |   371.806  510.34355 | 425.94611
           | 202.47547  192.80433 | 209.90691
           |      1933       1240 |      3173
-----------+----------------------+----------
      1995 | 609.86273  786.31743 | 689.34474
           | 326.85067  274.35932 | 316.68276
           |      1253       1027 |      2280
-----------+----------------------+----------
      1996 | 403.34934  916.68237 | 573.77426
           | 416.54539  430.22198 | 485.53652
           |      1666        828 |      2494
-----------+----------------------+----------
      1997 | 438.72757  876.81221 | 567.96861
           | 345.84267  574.85793 |  470.7674
           |      1527        639 |      2166
-----------+----------------------+----------
      1998 | 511.15948  1058.4868 | 677.16567
           | 414.11503   671.6638 | 565.20548
           |      1392        606 |      1998
-----------+----------------------+----------
      1999 |  505.3589  1008.6681 | 654.47558
           | 450.32896  728.88316 | 593.87291
           |      1095        461 |      1556
-----------+----------------------+----------
      2000 | 475.09549  1093.4316 | 651.83065
           | 540.93034   874.9704 | 710.92464
           |       932        373 |      1305
-----------+----------------------+----------
      2001 | 507.81273  1027.4596 | 679.76399
           | 564.18529  913.58706 | 740.41329
           |       550        272 |       822
-----------+----------------------+----------
      2002 | 553.89625  1245.1105 | 782.96917
           | 539.21861    1010.94 | 798.53536
           |       347        172 |       519
-----------+----------------------+----------
      2003 | 418.90515  1219.4357 | 639.09037
           | 560.05473  1165.2873 | 852.52805
           |       369        140 |       509
-----------+----------------------+----------
      2004 | 624.86129  1702.8515 | 889.76886
           | 811.57264  1269.1878 | 1051.2857
           |       310        101 |       411
-----------+----------------------+----------
      2005 | 613.85551  1200.4643 | 755.85879
           | 831.70328  1378.8593 |  1021.369
           |       263         NA |       347
-----------+----------------------+----------
      2006 | 581.15584  1595.9692 | 804.00338
           | 853.75194  1531.2897 | 1119.8606
           |       231         NA |       296
-----------+----------------------+----------
      2007 | 448.00976  1664.9783 | 671.03984
           | 720.09956  1640.1714 | 1062.9795
           |       205         NA |       251
-----------+----------------------+----------
      2008 | 667.09444  1696.4792 | 883.80702
           | 1133.5017  1841.6236 | 1375.5936
           |       180         NA |       228
-----------+----------------------+----------
      2009 | 687.58182   2295.303 | 1058.5944
           | 1173.0878  2383.8239 | 1673.0165
           |       110         NA |       143
-----------+----------------------+----------
      2010 | 551.09322  3251.1053 | 1208.7885
           | 1093.5339  2455.6102 |  1921.968
           |       118         NA |       156
-----------+----------------------+----------
      2011 | 457.49383  2267.7826 | 857.84615
           | 860.58659  2502.4562 |  1575.677
           |        NA         NA |       104
-----------+----------------------+----------
      2012 | 256.01493  813.41176 | 368.82143
           | 719.27806   1788.639 | 1038.6873
           |        NA         NA |        NA
-----------+----------------------+----------
      2013 | 335.36066  551.63636 | 368.40278
           | 536.93817  1547.1943 | 766.11492
           |        NA         NA |        NA
-----------+----------------------+----------
      2014 |     541.6      147.5 | 461.63768
           | 1171.1106  212.26063 | 1059.8275
           |        NA         NA |        NA
-----------+----------------------+----------
      2015 | 797.28302  1672.2727 | 947.67188
           | 1715.6879  2520.7049 | 1883.8501
           |        NA         NA |        NA
-----------+----------------------+----------
      2016 |  263.4902      760.5 | 358.15873
           | 592.78311  1772.9982 | 937.97954
           |        NA         NA |        NA
-----------+----------------------+----------
      2017 | 377.47273      207.6 | 351.33846
           | 750.39774  136.61479 | 693.93996
           |        NA         NA |        NA
-----------+----------------------+----------
     Total | 385.55137  762.06591 | 501.12066
           | 471.66099   779.6285 | 609.00498
           |     18122       8026 |     26148
r; t=16.04 14:51:51

.                         
.                         disp("Duration of Post lignite unemployment series followed by employment in NON lignite")
Duration of Post lignite unemployment series followed by employment in NON lignite
r; t=0.00 14:51:51

.                         sum dur if (posttrans==7), detail

                duration in labour mkt status
-------------------------------------------------------------
      Percentiles      Smallest
 1%            3              1
 5%           22              1
10%           45              1       Obs              22,273
25%          134              1       Sum of wgt.      22,273

50%          365                      Mean           556.7482
                        Largest       Std. dev.      635.6502
75%          776           6329
90%         1219           6393       Variance       404051.2
95%         1647           7152       Skewness       3.055947
99%         3044           7397       Kurtosis       18.61346
r; t=0.59 14:51:52

.                         disp("Duration of Post lignite unemployment series followed by employmentin Lignite")
Duration of Post lignite unemployment series followed by employmentin Lignite
r; t=0.00 14:51:52

.                         sum dur if (posttrans==8), detail

                duration in labour mkt status
-------------------------------------------------------------
      Percentiles      Smallest
 1%            1              1
 5%            3              1
10%            6              1       Obs               3,875
25%           35              1       Sum of wgt.       3,875

50%           98                      Mean           181.3806
                        Largest       Std. dev.      245.4952
75%          227           2111
90%          396           2141       Variance       60267.92
95%          664           2496       Skewness       3.866825
99%         1247           3957       Kurtosis       30.02782
r; t=0.57 14:51:52

.         
.                         disp("Duration of Post NON-lignite unemployment series followed by employment")
Duration of Post NON-lignite unemployment series followed by employment
r; t=0.00 14:51:52

.                         sum dur if (posttrans==11 | posttrans==12), detail      

                duration in labour mkt status
-------------------------------------------------------------
      Percentiles      Smallest
 1%            2              1
 5%            9              1
10%           18              1       Obs             176,395
25%           52              1       Sum of wgt.     176,395

50%          123                      Mean           290.6305
                        Largest       Std. dev.      485.7606
75%          310           6891
90%          730           7087       Variance       235963.4
95%         1139           7547       Skewness       4.432169
99%         2465           7716       Kurtosis       31.65966
r; t=0.81 14:51:53

.                         disp("Duration of Post NON-lignite unemployment series followed by employment in NON lignite")
Duration of Post NON-lignite unemployment series followed by employment in NON lignite
r; t=0.00 14:51:53

.                         sum dur if (posttrans==11), detail

                duration in labour mkt status
-------------------------------------------------------------
      Percentiles      Smallest
 1%            2              1
 5%            9              1
10%           18              1       Obs             171,901
25%           51              1       Sum of wgt.     171,901

50%          123                      Mean           291.7418
                        Largest       Std. dev.      489.1952
75%          311           6891
90%          731           7087       Variance       239311.9
95%         1149           7547       Skewness       4.418004
99%         2485           7716       Kurtosis       31.40176
r; t=0.70 14:51:54

.                         disp("Duration of Post NON-lignite unemployment series followed by employment in Lignite")
Duration of Post NON-lignite unemployment series followed by employment in Lignite
r; t=0.00 14:51:54

.                         sum dur if (posttrans==12), detail              

                duration in labour mkt status
-------------------------------------------------------------
      Percentiles      Smallest
 1%            3              1
 5%           15              1
10%           30              1       Obs               4,494
25%           64              1       Sum of wgt.       4,494

50%          150                      Mean           248.1213
                        Largest       Std. dev.      325.6487
75%          304           3287
90%          565           3374       Variance       106047.1
95%          822           3499       Skewness       4.038572
99%         1683           4748       Kurtosis       29.79977
r; t=0.55 14:51:54

.                         
.         /* ------------------------------------------------------------- */
.                 * 6.2 create variable for macro scenarios * 
.         /* ------------------------------------------------------------- */     
.                 
.                 g macro=.
(1,382,388 missing values generated)
r; t=0.03 14:51:54

.                 * bad macro conditions = 1 / good macro conditions = 2
.                 * approximations here
.                 * (1) good macro is less than 10% unemployment
.                 * (2)   Lausitz:    from (incl) 2015-
.                 *               Rheinisches from (incl) 2007 -
.                 *       Helmstedter from (incl) 2008 - 
.                 *               Mitteldeutsches from (incl) 2016 -
.                 replace macro=1 if mining_area== 1 & endepi<mdy(01,01,2015)
(233,459 real changes made)
r; t=0.06 14:51:55

.                 replace macro=1 if mining_area== 2 & endepi<mdy(01,01,2016)
(167,067 real changes made)
r; t=0.05 14:51:55

.                 replace macro=1 if mining_area== 3 & endepi<mdy(01,01,2008)
(18,618 real changes made)
r; t=0.05 14:51:55

.                 replace macro=1 if mining_area== 4 & endepi<mdy(01,01,2007)
(123,258 real changes made)
r; t=0.06 14:51:55

.                 
.                 replace macro=2 if mining_area== 1 & endepi>=mdy(01,01,2015)
(31,376 real changes made)
r; t=0.05 14:51:55

.                 replace macro=2 if mining_area== 2 & endepi>=mdy(01,01,2016)
(17,500 real changes made)
r; t=0.06 14:51:55

.                 replace macro=2 if mining_area== 3 & endepi>=mdy(01,01,2008)
(4,466 real changes made)
r; t=0.07 14:51:55

.                 replace macro=2 if mining_area== 4 & endepi>=mdy(01,01,2007)
(42,377 real changes made)
r; t=0.08 14:51:55

. 
.                 * West-Germany: Unemployment below 10% in all years since 1990 
.                 replace macro=2 if (mining_area==. | mining_area==5) & ao_bula<12
(640,574 real changes made)
r; t=0.09 14:51:55

. 
.                 * East Germany: Unemployment below 10% for women since 2012, for men since 2015
.                 replace macro=1 if (mining_area==. | mining_area==5) & ao_bula>=12 & endepi<mdy(01,01,2012) & frau==1
(19,988 real changes made)
r; t=0.10 14:51:55

.                 replace macro=1 if (mining_area==. | mining_area==5) & ao_bula>=12 & endepi<mdy(01,01,2014) & frau==0
(61,178 real changes made)
r; t=0.08 14:51:55

.                 replace macro=2 if (mining_area==. | mining_area==5) & ao_bula>=12 & endepi>=mdy(01,01,2012) & frau==1
(6,570 real changes made)
r; t=0.09 14:51:55

.                 replace macro=2 if (mining_area==. | mining_area==5) & ao_bula>=12 & endepi>=mdy(01,01,2014) & frau==0
(15,957 real changes made)
r; t=0.08 14:51:55

.                 label define macroLAB 1 "hi-unemp" 2 "lo-unemp" 
r; t=0.00 14:51:55

.                 label values macro macroLAB
r; t=0.00 14:51:55

. 
.                 if ${sample}==7{
.                 replace macro=.
r; t=0.00 14:51:55
.                 replace macro=1 if endepi<mdy(01,01,2000)
r; t=0.00 14:51:55
.                 replace macro=2 if endepi>=mdy(01,01,2000)
r; t=0.00 14:51:55
.                 }       
r; t=0.00 14:51:55

.                 if (${sample}==8 | ${sample}==9){
.                 replace macro=1
r; t=0.00 14:51:55
.                 label define macroLAB 1 "all macro-econ conditions", replace 
r; t=0.00 14:51:55
.                 label values macro macroLAB
r; t=0.00 14:51:55
.                 }       
r; t=0.00 14:51:55

.                 
.                 tab macro, m

      macro |      Freq.     Percent        Cum.
------------+-----------------------------------
   hi-unemp |    623,568       45.11       45.11
   lo-unemp |    758,820       54.89      100.00
------------+-----------------------------------
      Total |  1,382,388      100.00
r; t=0.24 14:51:56

. 
.                 /*
>                 *create a variable macrot_0 indicating the beginning of current macro regime
>                 g macro_t0=mdy(01,01,1900)
>                 format macro_t0 %tdD_m_CY
>                 * Lausitz:    change in 2015
>                 * Rheinisches change in 2007
>                 * Helmstedter change in 2008
>                 * Mitteldeutsches change in 2016
>                 replace macro_t0=mdy(01,01,2015) if mining_area== 1 & endepi>=mdy(01,01,2015)
>                 replace macro_t0=mdy(01,01,2016) if mining_area== 2 & endepi>=mdy(01,01,2016)   
>                 replace macro_t0=mdy(01,01,2008) if mining_area== 3 & endepi>=mdy(01,01,2008)
>                 replace macro_t0=mdy(01,01,2007) if mining_area== 4 & endepi>=mdy(01,01,2007)   
>                 
>                 * West-Germany (apart from Rhineland) no change over the period
>                 
>                 * East Germany:change in 2012 for women, 2015 for men
>                 replace macro_t0=mdy(01,01,2012) if (mining_area==. | mining_area==5) & ao_bula>=12 & frau==1 & endepi>=mdy(01,01,2012)
>                 replace macro_t0=mdy(01,01,2014) if (mining_area==. | mining_area==5) & ao_bula>=12 & frau==0 & endepi>=mdy(01,01,2014)                                 
>                 
>                 if ${sample}==7{
>                 replace macro_t0=mdy(01,01,2000)
>                 }
>                 
>                 tab macro_t0, m
>                 tab censor macro
>                 tab mining_area macro   
>                 */
.         /* ------------------------------------------------------------- */
.                 * 6.3 Recalculate age and decades at the end of spell
.         /* ------------------------------------------------------------- */     
.         tab ageend      

 age at end |
   of spell |      Freq.     Percent        Cum.
------------+-----------------------------------
         18 |     21,793        1.58        1.58
         19 |     28,318        2.05        3.62
         20 |     34,025        2.46        6.09
         21 |     34,932        2.53        8.61
         22 |     29,704        2.15       10.76
         23 |     26,246        1.90       12.66
         24 |     24,165        1.75       14.41
         25 |     23,858        1.73       16.13
         26 |     23,875        1.73       17.86
         27 |     24,061        1.74       19.60
         28 |     23,883        1.73       21.33
         29 |     24,271        1.76       23.09
         30 |     24,806        1.79       24.88
         31 |     25,196        1.82       26.70
         32 |     26,217        1.90       28.60
         33 |     27,003        1.95       30.55
         34 |     27,495        1.99       32.54
         35 |     28,319        2.05       34.59
         36 |     29,006        2.10       36.69
         37 |     29,267        2.12       38.81
         38 |     29,751        2.15       40.96
         39 |     30,103        2.18       43.14
         40 |     30,814        2.23       45.36
         41 |     31,421        2.27       47.64
         42 |     31,815        2.30       49.94
         43 |     32,519        2.35       52.29
         44 |     33,057        2.39       54.68
         45 |     33,209        2.40       57.08
         46 |     33,009        2.39       59.47
         47 |     32,491        2.35       61.82
         48 |     32,922        2.38       64.20
         49 |     32,811        2.37       66.58
         50 |     33,304        2.41       68.99
         51 |     34,787        2.52       71.50
         52 |     34,500        2.50       74.00
         53 |     34,898        2.52       76.52
         54 |     35,956        2.60       79.12
         55 |     41,774        3.02       82.15
         56 |     36,296        2.63       84.77
         57 |     33,429        2.42       87.19
         58 |     31,783        2.30       89.49
         59 |     26,847        1.94       91.43
         60 |     43,878        3.17       94.61
         61 |     18,292        1.32       95.93
         62 |     13,969        1.01       96.94
         63 |     15,616        1.13       98.07
         64 |      6,650        0.48       98.55
         65 |      5,864        0.42       98.97
         66 |      3,331        0.24       99.21
         67 |      2,436        0.18       99.39
         68 |      1,893        0.14       99.53
         69 |      1,532        0.11       99.64
         70 |      1,227        0.09       99.73
         71 |        943        0.07       99.80
         72 |        805        0.06       99.85
         73 |        667        0.05       99.90
         74 |        481        0.03       99.94
         75 |        358        0.03       99.96
         76 |        510        0.04      100.00
------------+-----------------------------------
      Total |  1,382,388      100.00
r; t=0.39 14:51:56

.         tab agebeg

     agebeg |      Freq.     Percent        Cum.
------------+-----------------------------------
         13 |         NA        0.00        0.00
         14 |         NA        0.00        0.00
         15 |        116        0.01        0.01
         16 |        260        0.02        0.03
         17 |      1,340        0.11        0.15
         18 |     23,807        2.03        2.18
         19 |     28,999        2.47        4.65
         20 |     34,842        2.97        7.62
         21 |     35,313        3.01       10.64
         22 |     27,748        2.37       13.00
         23 |     24,528        2.09       15.09
         24 |     22,351        1.91       17.00
         25 |     22,039        1.88       18.88
         26 |     22,379        1.91       20.79
         27 |     22,623        1.93       22.72
         28 |     22,746        1.94       24.66
         29 |     23,100        1.97       26.63
         30 |     23,666        2.02       28.65
         31 |     24,345        2.08       30.72
         32 |     25,103        2.14       32.86
         33 |     25,863        2.21       35.07
         34 |     26,530        2.26       37.33
         35 |     27,257        2.32       39.66
         36 |     27,985        2.39       42.04
         37 |     28,152        2.40       44.45
         38 |     28,801        2.46       46.90
         39 |     28,948        2.47       49.37
         40 |     29,592        2.52       51.90
         41 |     30,057        2.56       54.46
         42 |     30,085        2.57       57.02
         43 |     29,814        2.54       59.57
         44 |     29,437        2.51       62.08
         45 |     29,150        2.49       64.56
         46 |     28,437        2.43       66.99
         47 |     28,234        2.41       69.40
         48 |     28,314        2.41       71.81
         49 |     27,769        2.37       74.18
         50 |     27,763        2.37       76.55
         51 |     28,472        2.43       78.98
         52 |     28,000        2.39       81.37
         53 |     27,588        2.35       83.72
         54 |     27,901        2.38       86.10
         55 |     32,088        2.74       88.84
         56 |     31,332        2.67       91.51
         57 |     24,657        2.10       93.61
         58 |     20,279        1.73       95.34
         59 |     15,477        1.32       96.66
         60 |     11,057        0.94       97.60
         61 |      7,838        0.67       98.27
         62 |      5,738        0.49       98.76
         63 |      4,430        0.38       99.14
         64 |      2,701        0.23       99.37
         65 |      1,926        0.16       99.53
         66 |      1,354        0.12       99.65
         67 |      1,014        0.09       99.74
         68 |        753        0.06       99.80
         69 |        659        0.06       99.86
         70 |        461        0.04       99.90
         71 |        374        0.03       99.93
         72 |        288        0.02       99.95
         73 |        238        0.02       99.97
         74 |        155        0.01       99.99
         75 |        114        0.01      100.00
         76 |         NA        0.00      100.00
------------+-----------------------------------
      Total |  1,172,457      100.00
r; t=0.39 14:51:57

.         cap drop jahrend
r; t=0.27 14:51:57

.         cap drop jahrbeg
r; t=0.26 14:51:57

.         gen jahrend = year(endepi)
r; t=0.06 14:51:57

.         gen jahrbeg = year(begepi)
r; t=0.07 14:51:57

.         label var jahrend "year at end of spell"
r; t=0.00 14:51:57

.         label var jahrbeg "year at end of spell"
r; t=0.00 14:51:57

.         replace ageend = jahrend - year(geb_dat)
(45,982 real changes made)
r; t=0.39 14:51:58

.         replace agebeg = jahrbeg - year(geb_dat)
(316,764 real changes made)
r; t=0.48 14:51:58

.         label var ageend "age at end of spell"
r; t=0.00 14:51:58

.         label var agebeg "age at start of spell"
r; t=0.00 14:51:58

.         di "tab age at end of last spell of sampled workers"
tab age at end of last spell of sampled workers
r; t=0.00 14:51:58

.         tab ageend, m 

 age at end |
   of spell |      Freq.     Percent        Cum.
------------+-----------------------------------
         14 |         NA        0.00        0.00
         15 |         NA        0.00        0.00
         16 |         NA        0.00        0.00
         17 |         NA        0.00        0.00
         18 |     21,792        1.58        1.58
         19 |     28,342        2.05        3.63
         20 |     34,091        2.47        6.10
         21 |     35,049        2.54        8.63
         22 |     29,790        2.15       10.79
         23 |     26,373        1.91       12.69
         24 |     24,345        1.76       14.45
         25 |     24,014        1.74       16.19
         26 |     24,030        1.74       17.93
         27 |     24,139        1.75       19.68
         28 |     23,927        1.73       21.41
         29 |     24,395        1.76       23.17
         30 |     24,879        1.80       24.97
         31 |     25,270        1.83       26.80
         32 |     26,277        1.90       28.70
         33 |     27,007        1.95       30.65
         34 |     27,609        2.00       32.65
         35 |     28,398        2.05       34.71
         36 |     29,080        2.10       36.81
         37 |     29,459        2.13       38.94
         38 |     29,987        2.17       41.11
         39 |     30,429        2.20       43.31
         40 |     31,325        2.27       45.58
         41 |     32,216        2.33       47.91
         42 |     32,560        2.36       50.26
         43 |     33,018        2.39       52.65
         44 |     33,459        2.42       55.07
         45 |     33,615        2.43       57.50
         46 |     33,433        2.42       59.92
         47 |     33,030        2.39       62.31
         48 |     33,424        2.42       64.73
         49 |     33,258        2.41       67.13
         50 |     33,624        2.43       69.57
         51 |     35,051        2.54       72.10
         52 |     34,613        2.50       74.61
         53 |     34,650        2.51       77.11
         54 |     35,334        2.56       79.67
         55 |     41,053        2.97       82.64
         56 |     35,489        2.57       85.21
         57 |     32,736        2.37       87.57
         58 |     31,171        2.25       89.83
         59 |     26,222        1.90       91.72
         60 |     43,480        3.15       94.87
         61 |     17,831        1.29       96.16
         62 |     13,212        0.96       97.12
         63 |     14,082        1.02       98.13
         64 |      6,522        0.47       98.61
         65 |      5,576        0.40       99.01
         66 |      3,237        0.23       99.24
         67 |      2,389        0.17       99.42
         68 |      1,818        0.13       99.55
         69 |      1,508        0.11       99.66
         70 |      1,196        0.09       99.74
         71 |        933        0.07       99.81
         72 |        775        0.06       99.87
         73 |        640        0.05       99.91
         74 |        449        0.03       99.95
         75 |        339        0.02       99.97
         76 |        406        0.03      100.00
------------+-----------------------------------
      Total |  1,382,388      100.00
r; t=0.48 14:51:59

. 
.         cap drop agecat2
r; t=0.22 14:51:59

.         gen agecat2=.
(1,382,388 missing values generated)
r; t=0.04 14:51:59

.         replace agecat2 = 1 if ageend >= 18 & ageend <= 30
(345,166 real changes made)
r; t=0.05 14:51:59

.         replace agecat2 = 2 if ageend > 30 & ageend <= 49
(582,854 real changes made)
r; t=0.06 14:51:59

.         replace agecat2 = 3 if ageend > 49 & ageend !=. 
(454,336 real changes made)
r; t=0.05 14:51:59

.         tab agecat2, m

    agecat2 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |    345,166       24.97       24.97
          2 |    582,854       42.16       67.13
          3 |    454,336       32.87      100.00
          . |         NA        0.00      100.00
------------+-----------------------------------
      Total |  1,382,388      100.00
r; t=0.23 14:51:59

.         
.         * Decade of end of spell (decade 1 are actually more than two decades)
.         cap drop decades
r; t=0.22 14:51:59

.         g decades=.
(1,382,388 missing values generated)
r; t=0.04 14:51:59

.         replace decades=1 if endepi>=mdy(01,01,1970) & endepi<mdy(01,01,1992)
(148,684 real changes made)
r; t=0.07 14:52:00

. 
.         * note post-92 (incl. East Germany)
.         replace decades=2 if endepi>=mdy(01,01,1992) & endepi<mdy(01,01,2000)
(443,532 real changes made)
r; t=0.08 14:52:00

.         replace decades=3 if endepi>=mdy(01,01,2000) & endepi<mdy(01,01,2010)
(464,599 real changes made)
r; t=0.08 14:52:00

.         replace decades=4 if endepi>=mdy(01,01,2010) & endepi!=.
(325,571 real changes made)
r; t=0.06 14:52:00

.         
.         tab decades, m  

    decades |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |    148,684       10.76       10.76
          2 |    443,532       32.08       42.84
          3 |    464,599       33.61       76.45
          4 |    325,571       23.55      100.00
          . |         NA        0.00      100.00
------------+-----------------------------------
      Total |  1,382,388      100.00
r; t=0.23 14:52:00

.         
.                 
.         /* ------------------------------------------------------------------------ */
.                 *  6.4 Exposure time for estimation
.         /* ------------------------------------------------------------------------ */  
. 
.                 * Identify spells after person enter in 103, keeping date of enter for the spells after entering
.                         cap drop enter103
r; t=0.00 14:52:00

.                         gen enter103=.
(1,382,388 missing values generated)
r; t=0.03 14:52:00

.                         bys persnr (first103): replace enter103=first103[1]
(30428 real changes made)
r; t=0.64 14:52:01

.                         format enter103 %tdDDmonYY
r; t=0.00 14:52:01

.         
.                 * Identify Employment periods in Lignite 
.                         cap drop Emp_Lig
r; t=0.00 14:52:01

.                         gen Emp_Lig=0
r; t=0.04 14:52:01

.                         bysort pid (begepi): replace Emp_Lig=1 if statsimple==1 & thisspelllignite== 1  
(189807 real changes made)
r; t=0.75 14:52:01

.                         di "check pretrans when employed in lignite"
check pretrans when employed in lignite
r; t=0.00 14:52:01

.                         tab pretrans Emp_Lig

                      |        Emp_Lig
             pretrans |         0          1 |     Total
----------------------+----------------------+----------
Not pre (observed) tr | 1,008,715     63,808 | 1,072,523 
pre retirement (witho |        NA     10,481 |    10,558 
pre trans'n 2 vocatio |         0        163 |       163 
pre trans'n 2 other n |         0     25,088 |    25,088 
pre trans'n to unem/A |         0     14,760 |    14,760 
pre trans'n 2 unemp/A |         0     22,349 |    22,349 
pre trans'n 2 unemp/A |         0      3,935 |     3,935 
pre trans'n 2 black h |     5,603     49,223 |    54,826 
pre transition out of |   173,673          0 |   173,673 
pre transition out of |     4,513          0 |     4,513 
----------------------+----------------------+----------
                Total | 1,192,581    189,807 | 1,382,388 
r; t=0.40 14:52:02

.                 
.                 * Identify Employment periods NOT in Lignite
.                         cap drop Emp_NonLig
r; t=0.00 14:52:02

.                         gen Emp_NonLig=0
r; t=0.12 14:52:02

.                         bysort pid (begepi): replace Emp_NonLig=1 if statsimple==1 & thisspelllignite==0
(464423 real changes made)
r; t=0.12 14:52:02

.                         di "check pretrans when employed in non lignite"
check pretrans when employed in non lignite
r; t=0.00 14:52:02

.                         tab pretrans Emp_NonLig         

                      |      Emp_NonLig
             pretrans |         0          1 |     Total
----------------------+----------------------+----------
Not pre (observed) tr |   786,286    286,237 | 1,072,523 
pre retirement (witho |    10,558          0 |    10,558 
pre trans'n 2 vocatio |       163          0 |       163 
pre trans'n 2 other n |    25,088          0 |    25,088 
pre trans'n to unem/A |    14,760          0 |    14,760 
pre trans'n 2 unemp/A |    22,349          0 |    22,349 
pre trans'n 2 unemp/A |     3,935          0 |     3,935 
pre trans'n 2 black h |    54,826          0 |    54,826 
pre transition out of |         0    173,673 |   173,673 
pre transition out of |         0      4,513 |     4,513 
----------------------+----------------------+----------
                Total |   917,965    464,423 | 1,382,388 
r; t=0.40 14:52:03

.                         
.                 * Identify Unemployment periods after Employment in Lignite
.                         cap drop Unemp_postLig
r; t=0.00 14:52:03

.                         gen Unemp_postLig=0
r; t=0.03 14:52:03

.                         bysort pid (begepi): replace Unemp_postLig =1 if statsimple==0 & statsimple[_n-1]==1 & thisspelllignite[_n-1]== 1
(54122 real changes made)
r; t=0.10 14:52:03

.                         di "check pretrans when unemployed after lignite"
check pretrans when unemployed after lignite
r; t=0.00 14:52:03

.                         tab pretrans Unemp_postLig                      

                      |     Unemp_postLig
             pretrans |         0          1 |     Total
----------------------+----------------------+----------
Not pre (observed) tr | 1,019,742     52,781 | 1,072,523 
pre retirement (witho |    10,554          4 |    10,558 
pre trans'n 2 vocatio |       163          0 |       163 
pre trans'n 2 other n |    25,088          0 |    25,088 
pre trans'n to unem/A |    14,760          0 |    14,760 
pre trans'n 2 unemp/A |    22,349          0 |    22,349 
pre trans'n 2 unemp/A |     3,935          0 |     3,935 
pre trans'n 2 black h |    53,489      1,337 |    54,826 
pre transition out of |   173,673          0 |   173,673 
pre transition out of |     4,513          0 |     4,513 
----------------------+----------------------+----------
                Total | 1,328,266     54,122 | 1,382,388 
r; t=0.40 14:52:03

.                         di "check postrans when unemployed after lignite"
check postrans when unemployed after lignite
r; t=0.00 14:52:03

.                         tab posttrans Unemp_postLig

                      |     Unemp_postLig
            posttrans |         0          1 |     Total
----------------------+----------------------+----------
Not post trans'n out  | 1,066,729     13,043 | 1,079,772 
in retirement (no min |       120        229 |       349 
 post-lignite vocatio |       158          0 |       158 
post-lignite normal e |    32,968          0 |    32,968 
post-lignite unemp/AL |        NA     14,706 |    14,711 
post-lignite unemp/AL |        NA     22,271 |    22,273 
post-lignite unemp/AL |        NA      3,873 |     3,875 
post-lignite black ho |    51,887          0 |    51,887 
transition out of NON |   171,901          0 |   171,901 
transition out of NON |     4,494          0 |     4,494 
----------------------+----------------------+----------
                Total | 1,328,266     54,122 | 1,382,388 
r; t=0.41 14:52:03

. 
.                 * Identify Unemployment periods after Employment NOT in Lignite
.                         cap drop Unemp_postNonLig
r; t=0.00 14:52:03

.                         g Unemp_postNonLig=0
r; t=0.04 14:52:04

.                         bysort pid (begepi): replace Unemp_postNonLig = 1 if statsimple==0 & statsimple[_n-1]==1 & thisspelllignite[_n-1]== 0   
(228811 real changes made)
r; t=0.09 14:52:04

.                         di "check pretrans when unemployed after non lignite"
check pretrans when unemployed after non lignite
r; t=0.00 14:52:04

.                         tab pretrans Unemp_postNonLig                   

                      |   Unemp_postNonLig
             pretrans |         0          1 |     Total
----------------------+----------------------+----------
Not pre (observed) tr |   843,721    228,802 | 1,072,523 
pre retirement (witho |    10,558          0 |    10,558 
pre trans'n 2 vocatio |       163          0 |       163 
pre trans'n 2 other n |    25,088          0 |    25,088 
pre trans'n to unem/A |    14,760          0 |    14,760 
pre trans'n 2 unemp/A |    22,349          0 |    22,349 
pre trans'n 2 unemp/A |     3,935          0 |     3,935 
pre trans'n 2 black h |    54,817          9 |    54,826 
pre transition out of |   173,673          0 |   173,673 
pre transition out of |     4,513          0 |     4,513 
----------------------+----------------------+----------
                Total | 1,153,577    228,811 | 1,382,388 
r; t=0.42 14:52:04

.                         di "check postrans when unemployed after non lignite"
check postrans when unemployed after non lignite
r; t=0.00 14:52:04

.                         tab posttrans Unemp_postNonLig                  

                      |   Unemp_postNonLig
            posttrans |         0          1 |     Total
----------------------+----------------------+----------
Not post trans'n out  | 1,027,218     52,554 | 1,079,772 
in retirement (no min |       349          0 |       349 
 post-lignite vocatio |       158          0 |       158 
post-lignite normal e |    32,968          0 |    32,968 
post-lignite unemp/AL |    14,711          0 |    14,711 
post-lignite unemp/AL |    22,273          0 |    22,273 
post-lignite unemp/AL |     3,875          0 |     3,875 
post-lignite black ho |    51,887          0 |    51,887 
transition out of NON |       137    171,764 |   171,901 
transition out of NON |        NA      4,493 |     4,494 
----------------------+----------------------+----------
                Total | 1,153,577    228,811 | 1,382,388 
r; t=0.43 14:52:04

.                 
.                 * for rho (ex-mu) : Time in employment in lignite, including ATZ
.                         * When ATZ case (person103==1): end of duration of employment is mid103 instead of endepi
.                         * We then redefine the duration at risk : durretrisk
. 
.                         * create new variable counting start of spell or start of old-age period,
.                         * because we do not want to diminish retirement risk in old-age by 
.                         * adding time at risk pre-50 years of age
. 
.                         g begepiret=begepi
r; t=0.06 14:52:05

.                         * only if spell started before 50th birthday 
.                         * & person is over 50 at end => take 50 as start date of spell.
.                         label var begepiret "begepi for cell-estimation of retirement probs (based on 50-year-cut-off)"
r; t=0.00 14:52:05

.                         replace begepiret =mdy(month(geb_dat),day(geb_dat),year(geb_dat)+50) if (ageend >=50 & agebeg<50)
(77,300 real changes made, 49 to missing)
r; t=0.05 14:52:05

.                         *if spell started after 50 years of age, keep original spell start.
.                         * if spell started before 50 years of age and 
.                         *       => take date of 50th birthday as start of spell
.                         format begepiret %tdDDmonYY
r; t=0.00 14:52:05

. 
.                         gen durretrisk=dur
r; t=0.06 14:52:05

.                         replace durretrisk=(mid103-begepiret)+1 if (person103==1) & (mid103>begepiret) & (mid103<endepi)
(4,160 real changes made)
r; t=0.05 14:52:05

.                         * zero risk of retirement once you are beyond mid-point of ATZ
.                         * (you are basically already retired, only legally not.)
.                         replace durretrisk=0 if (person103==1 & mid103<begepiret)
(3,149 real changes made)
r; t=0.05 14:52:05

.                         sum durretrisk, detail  

                         durretrisk
-------------------------------------------------------------
      Percentiles      Smallest
 1%            3              0
 5%           22              0
10%           35              0       Obs           1,382,388
25%           94              0       Sum of wgt.   1,382,388

50%          305                      Mean           759.9919
                        Largest       Std. dev.       1277.06
75%          786          14334
90%         1983          14346       Variance        1630882
95%         3378          14513       Skewness        3.47212
99%         6585          14640       Kurtosis       18.15695
r; t=2.51 14:52:07

.                         * on average, how much time are lignite workers at risk of retirement
.                         sum durretrisk if Emp_Lig==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
  durretrisk |    189,807    1814.596    2134.586          0      14513
r; t=0.22 14:52:08

.                         * on average, how much time are lignite workers at risk before they directly retire?
.                         sum durretrisk if Emp_Lig==1 & (end==1 & pretrans==1)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
  durretrisk |      9,361    2583.809    2142.189          0      10500
r; t=0.55 14:52:08

.                         * on average, how much time are lignite workers at risk before they indirectly retire?
.                         sum durretrisk if Emp_Lig==1 & (end==1 & pretrans==6)   

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
  durretrisk |     14,708    2427.386    2390.573          1      12053
r; t=0.56 14:52:09

.                         * on average, how much time are lignite workers at risk if they are censored (no transition)
.                         sum durretrisk if Emp_Lig==1 & end==0           

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
  durretrisk |     78,386    2434.988    2512.509          0      14513
r; t=0.38 14:52:09

.                         
.                 * for delta and lambda: Time in employment in lignite or non lignite, excluding ATZ                     
.                         gen durrisk=dur
r; t=0.08 14:52:09

.                         replace durrisk=0 if person103==1 & begepi>=enter103                            
(7,162 real changes made)
r; t=0.05 14:52:09

.                         replace durrisk=(enter103-begepi)+1 if person103==1 & enter103>begepi & enter103<endepi                 
(2,073 real changes made)
r; t=0.07 14:52:09

.                         
.                 * for delta: Time in employment in lignite/non lignite excluding ATZ
.                         di "check durrisk for deltalig - Time in employment in lignite "
check durrisk for deltalig - Time in employment in lignite 
r; t=0.00 14:52:09

.                         sum durrisk if Emp_Lig==1                       

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
     durrisk |    189,807    1819.215    2154.996          0      14513
r; t=0.24 14:52:09

.                         sum durrisk if Emp_Lig==1 & (end==1 & pretrans==7) 

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
     durrisk |     22,273    882.7734    952.5208          1      10774
r; t=0.59 14:52:10

.                         sum durrisk if Emp_Lig==1 & (end==1 & pretrans==8)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
     durrisk |      3,873    683.5551    1078.508          0      10757
r; t=0.61 14:52:11

.                         sum durrisk if Emp_Lig==1 &  end==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
     durrisk |     78,386     2439.54    2538.233          0      14513
r; t=0.33 14:52:11

.                         
.                         di "check durrisk for deltanonlig - Time in employment non lignite"
check durrisk for deltanonlig - Time in employment non lignite
r; t=0.00 14:52:11

.                         sum durrisk if Emp_NonLig==1                                                                            

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
     durrisk |    464,423    861.1346    1252.106          0      14334
r; t=0.27 14:52:11

.                         sum durrisk if Emp_NonLig==1 & (end==1 & pretrans==11)                          

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
     durrisk |    171,765    602.9657    934.7922          0      13027
r; t=0.60 14:52:12

.                         sum durrisk if Emp_NonLig==1 & (end==1 & pretrans==12)                          

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
     durrisk |      4,493    665.2279     994.858          1      12744
r; t=0.64 14:52:13

.                         sum durrisk if Emp_NonLig==1 &  end==0                                                          

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
     durrisk |    140,055    1265.118    1610.675          0      14334
r; t=0.40 14:52:13

.                         
.                 * for lambda: Time in unemployment after lignite/non lignite excluding ATZ
.                         di "check durrisk for lambdalig - Time in unemployment after lignite"           
check durrisk for lambdalig - Time in unemployment after lignite
r; t=0.00 14:52:13

.                         sum durrisk if Unemp_postLig==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
     durrisk |     54,122    780.4159    866.2341          0       9406
r; t=0.25 14:52:13

.                         sum durrisk if Unemp_postLig==1 & (end==1 & posttrans==7)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
     durrisk |     22,271    556.7763    635.6737          0       7397
r; t=0.67 14:52:14

.                         sum durrisk if Unemp_postLig==1 & (end==1 & posttrans==8)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
     durrisk |      3,871    180.6531     245.573          0       3957
r; t=0.66 14:52:15

.                         sum durrisk if Unemp_postLig==1 &  end==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
     durrisk |     14,437    787.8956    1075.601          0       9406
r; t=0.37 14:52:15

.                         
.                         di "check durrisk for lambdalig - Time in unemployment after non lignite"
check durrisk for lambdalig - Time in unemployment after non lignite
r; t=0.00 14:52:15

.                         tab posttrans Unemp_postNonLig                                          

                      |   Unemp_postNonLig
            posttrans |         0          1 |     Total
----------------------+----------------------+----------
Not post trans'n out  | 1,027,218     52,554 | 1,079,772 
in retirement (no min |       349          0 |       349 
 post-lignite vocatio |       158          0 |       158 
post-lignite normal e |    32,968          0 |    32,968 
post-lignite unemp/AL |    14,711          0 |    14,711 
post-lignite unemp/AL |    22,273          0 |    22,273 
post-lignite unemp/AL |     3,875          0 |     3,875 
post-lignite black ho |    51,887          0 |    51,887 
transition out of NON |       137    171,764 |   171,901 
transition out of NON |        NA      4,493 |     4,494 
----------------------+----------------------+----------
                Total | 1,153,577    228,811 | 1,382,388 
r; t=0.45 14:52:15

.                         sum durrisk if Unemp_postNonLig==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
     durrisk |    228,811    416.8525    753.2259          0       9994
r; t=0.25 14:52:16

.                         sum durrisk if Unemp_postNonLig==1 & (end==1 & posttrans==11)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
     durrisk |    171,627    291.3345    488.6265          0       7716
r; t=0.61 14:52:16

.                         sum durrisk if Unemp_postNonLig==1 & (end==1 & posttrans==12)

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
     durrisk |      4,492    247.9435    325.5323          1       4748
r; t=0.64 14:52:17

.                         sum durrisk if Unemp_postNonLig==1 &  end==0

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
     durrisk |     51,625     838.259    1198.179          0       9994
r; t=0.35 14:52:17

.                                         
. /* ------------------------------------------------------------------------ */
.  *  (7) Cell sizes
.  * To choose cell sizes: How many obs per type & how many transitions ?
. /* ------------------------------------------------------------------------ */  
.                                 
. /* Sample==2 
>  Cell groups (2*2*3*2 = 24 cells)
> - gender (2 values)
> - education (2 values)
> - age category (3 values)
> - macro conditions (2 values)
> 
>  NB not: sector (3 values); mining area (2 values); tenure; occupations
> */      
. 
. /* ------------------------------------------------------------------------ */
.  *      CHECK MISSING FOR CHARACTERISTICS DEFINING CELLS
. /* ------------------------------------------------------------------------ */
. * All cells
. tab ageend, m

 age at end |
   of spell |      Freq.     Percent        Cum.
------------+-----------------------------------
         14 |         NA        0.00        0.00
         15 |         NA        0.00        0.00
         16 |         NA        0.00        0.00
         17 |         NA        0.00        0.00
         18 |     21,792        1.58        1.58
         19 |     28,342        2.05        3.63
         20 |     34,091        2.47        6.10
         21 |     35,049        2.54        8.63
         22 |     29,790        2.15       10.79
         23 |     26,373        1.91       12.69
         24 |     24,345        1.76       14.45
         25 |     24,014        1.74       16.19
         26 |     24,030        1.74       17.93
         27 |     24,139        1.75       19.68
         28 |     23,927        1.73       21.41
         29 |     24,395        1.76       23.17
         30 |     24,879        1.80       24.97
         31 |     25,270        1.83       26.80
         32 |     26,277        1.90       28.70
         33 |     27,007        1.95       30.65
         34 |     27,609        2.00       32.65
         35 |     28,398        2.05       34.71
         36 |     29,080        2.10       36.81
         37 |     29,459        2.13       38.94
         38 |     29,987        2.17       41.11
         39 |     30,429        2.20       43.31
         40 |     31,325        2.27       45.58
         41 |     32,216        2.33       47.91
         42 |     32,560        2.36       50.26
         43 |     33,018        2.39       52.65
         44 |     33,459        2.42       55.07
         45 |     33,615        2.43       57.50
         46 |     33,433        2.42       59.92
         47 |     33,030        2.39       62.31
         48 |     33,424        2.42       64.73
         49 |     33,258        2.41       67.13
         50 |     33,624        2.43       69.57
         51 |     35,051        2.54       72.10
         52 |     34,613        2.50       74.61
         53 |     34,650        2.51       77.11
         54 |     35,334        2.56       79.67
         55 |     41,053        2.97       82.64
         56 |     35,489        2.57       85.21
         57 |     32,736        2.37       87.57
         58 |     31,171        2.25       89.83
         59 |     26,222        1.90       91.72
         60 |     43,480        3.15       94.87
         61 |     17,831        1.29       96.16
         62 |     13,212        0.96       97.12
         63 |     14,082        1.02       98.13
         64 |      6,522        0.47       98.61
         65 |      5,576        0.40       99.01
         66 |      3,237        0.23       99.24
         67 |      2,389        0.17       99.42
         68 |      1,818        0.13       99.55
         69 |      1,508        0.11       99.66
         70 |      1,196        0.09       99.74
         71 |        933        0.07       99.81
         72 |        775        0.06       99.87
         73 |        640        0.05       99.91
         74 |        449        0.03       99.95
         75 |        339        0.02       99.97
         76 |        406        0.03      100.00
------------+-----------------------------------
      Total |  1,382,388      100.00
r; t=0.47 14:52:18

. tab agecat2, m          

    agecat2 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |    345,166       24.97       24.97
          2 |    582,854       42.16       67.13
          3 |    454,336       32.87      100.00
          . |         NA        0.00      100.00
------------+-----------------------------------
      Total |  1,382,388      100.00
r; t=0.26 14:52:18

. tab frau, m

       frau |      Freq.     Percent        Cum.
------------+-----------------------------------
       mann |  1,080,157       78.14       78.14
       frau |    302,231       21.86      100.00
------------+-----------------------------------
      Total |  1,382,388      100.00
r; t=0.21 14:52:18

. tab educ2, m

        education 2 |
         categories |      Freq.     Percent        Cum.
--------------------+-----------------------------------
keine abg. Ausbild. |    172,412       12.47       12.47
    abg. Ausbildung |  1,184,950       85.72       98.19
                  . |     25,026        1.81      100.00
--------------------+-----------------------------------
              Total |  1,382,388      100.00
r; t=0.29 14:52:18

. tab macro, m

      macro |      Freq.     Percent        Cum.
------------+-----------------------------------
   hi-unemp |    623,568       45.11       45.11
   lo-unemp |    758,820       54.89      100.00
------------+-----------------------------------
      Total |  1,382,388      100.00
r; t=0.31 14:52:19

. 
. * Only men (cells 1 to 12)
. tab ageend if frau==0, m

 age at end |
   of spell |      Freq.     Percent        Cum.
------------+-----------------------------------
         15 |         NA        0.00        0.00
         16 |         NA        0.00        0.00
         17 |         NA        0.00        0.00
         18 |     19,080        1.77        1.77
         19 |     24,065        2.23        4.00
         20 |     28,838        2.67        6.67
         21 |     30,330        2.81        9.47
         22 |     25,125        2.33       11.80
         23 |     21,947        2.03       13.83
         24 |     19,912        1.84       15.68
         25 |     19,277        1.78       17.46
         26 |     19,102        1.77       19.23
         27 |     18,907        1.75       20.98
         28 |     18,509        1.71       22.69
         29 |     18,609        1.72       24.42
         30 |     18,966        1.76       26.17
         31 |     19,138        1.77       27.94
         32 |     19,846        1.84       29.78
         33 |     20,607        1.91       31.69
         34 |     21,019        1.95       33.63
         35 |     21,537        1.99       35.63
         36 |     22,098        2.05       37.67
         37 |     22,555        2.09       39.76
         38 |     22,846        2.12       41.88
         39 |     23,173        2.15       44.02
         40 |     23,802        2.20       46.23
         41 |     24,638        2.28       48.51
         42 |     24,724        2.29       50.80
         43 |     25,031        2.32       53.11
         44 |     25,316        2.34       55.46
         45 |     25,435        2.35       57.81
         46 |     25,205        2.33       60.14
         47 |     24,918        2.31       62.45
         48 |     25,245        2.34       64.79
         49 |     25,156        2.33       67.12
         50 |     25,322        2.34       69.46
         51 |     26,887        2.49       71.95
         52 |     26,259        2.43       74.38
         53 |     26,528        2.46       76.84
         54 |     27,136        2.51       79.35
         55 |     31,550        2.92       82.27
         56 |     27,490        2.55       84.82
         57 |     25,815        2.39       87.21
         58 |     24,924        2.31       89.51
         59 |     21,100        1.95       91.47
         60 |     33,819        3.13       94.60
         61 |     14,537        1.35       95.94
         62 |     10,652        0.99       96.93
         63 |     11,656        1.08       98.01
         64 |      5,347        0.50       98.50
         65 |      4,578        0.42       98.93
         66 |      2,671        0.25       99.18
         67 |      1,960        0.18       99.36
         68 |      1,541        0.14       99.50
         69 |      1,294        0.12       99.62
         70 |      1,022        0.09       99.71
         71 |        820        0.08       99.79
         72 |        688        0.06       99.85
         73 |        573        0.05       99.91
         74 |        373        0.03       99.94
         75 |        286        0.03       99.97
         76 |        351        0.03      100.00
------------+-----------------------------------
      Total |  1,080,157      100.00
r; t=0.61 14:52:19

. tab agecat2 if frau==0, m               

    agecat2 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |    282,667       26.17       26.17
          2 |    442,289       40.95       67.12
          3 |    355,179       32.88      100.00
          . |         NA        0.00      100.00
------------+-----------------------------------
      Total |  1,080,157      100.00
r; t=0.54 14:52:20

. tab educ2 if frau==0, m

        education 2 |
         categories |      Freq.     Percent        Cum.
--------------------+-----------------------------------
keine abg. Ausbild. |    143,692       13.30       13.30
    abg. Ausbildung |    919,222       85.10       98.40
                  . |     17,243        1.60      100.00
--------------------+-----------------------------------
              Total |  1,080,157      100.00
r; t=0.53 14:52:20

. tab macro if frau==0, m

      macro |      Freq.     Percent        Cum.
------------+-----------------------------------
   hi-unemp |    487,369       45.12       45.12
   lo-unemp |    592,788       54.88      100.00
------------+-----------------------------------
      Total |  1,080,157      100.00
r; t=0.55 14:52:21

. 
. * Note that observations by same individuals may
. * now be in different cells (agecat, macro)
. if ${sample}<7{
. cap drop cell
r; t=0.00 14:52:21
.         if ${iab}==0{
.         egen cell=group(frau educ2 agecat2 macro), label
r; t=0.00 14:52:21
.         }
r; t=0.00 14:52:21
.         if ${iab}==1{
.         egen cell=group(frau educ2 agecat2 macro), label(cell, replace)
(25,058 missing values generated)
r; t=2.92 14:52:24
.         }
r; t=2.93 14:52:24
. global cellnumber "24"
r; t=0.00 14:52:24
. }
r; t=2.94 14:52:24

. 
. if ${sample}==7{
. * JAERE sample with data post-2000
. cap drop cell
r; t=0.00 14:52:24
.         if ${iab}==0{
.         egen cell=group(macro), label
r; t=0.00 14:52:24
.         }
r; t=0.00 14:52:24
.         if ${iab}==1{
.         egen cell=group(macro), label(cell, replace)
r; t=0.00 14:52:24
.         }
r; t=0.00 14:52:24
. label define cell 1 "pre-2000" 2 "post-2000", replace
r; t=0.00 14:52:24
. label val cell cell
r; t=0.00 14:52:24
. global cellnumber "2"
r; t=0.00 14:52:24
. }
r; t=0.01 14:52:24

. 
. if ${sample}==8{
. * JAERE sample for different occupations
. cap drop cell
r; t=0.00 14:52:24
.         if ${iab}==0{
.         egen cell=group(beruf12), label
r; t=0.00 14:52:24
.         }
r; t=0.00 14:52:24
.         if ${iab}==1{
.         egen cell=group(beruf12), label(cell, replace)
r; t=0.00 14:52:24
.         }
r; t=0.00 14:52:24
.         global cellnumber "11"
r; t=0.00 14:52:24
. }
r; t=0.01 14:52:24

. if ${sample}==9{
. * JAERE sample for different regions
. cap drop cell
r; t=0.00 14:52:24
. gen cell=.
r; t=0.00 14:52:24
. * cells 1-4 = four mining areas (1=Lausitz, 2=Mitteld., 3=Helmstedt, 4=Rheinisch, 5=Other) 
. replace cell = mining_area
r; t=0.01 14:52:24
. * cell 5 = West Germany
. replace cell = 5 if (mining_area==. | mining_area==5) & ao_bula < 12
r; t=0.00 14:52:24
. * cell 6 = East Germany
. replace cell = 6 if (mining_area==. | mining_area==5) & (ao_bula>12 & ao_bula<.)
r; t=0.00 14:52:24
. global cellnumber "6"
r; t=0.00 14:52:24
. label define cell 1 "Lausitzer Revier" 2 "Mitteldt. Revier" 3 "Helmstedter Revier" 4 "Rheinisches Revier" 5 "Other West Ger" 6 "Other East Ger"
r; t=0.00 14:52:24
. label val cell cell
r; t=0.00 14:52:24
. }
r; t=0.02 14:52:24

. 
.                         *7a) Number of spells in cells 
.                         di "total number of spells in cells: education, age, unemp, gender"
total number of spells in cells: education, age, unemp, gender
r; t=0.00 14:52:24

.                         describe cell

Variable      Storage   Display    Value
    name         type    format    label      Variable label
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
cell            float   %35.0g     cell       group(frau educ2 agecat2 macro)
r; t=0.00 14:52:24

.                         label list cell
cell:
           1 mann keine abg. Ausbild. 1 hi-unemp
           2 mann keine abg. Ausbild. 1 lo-unemp
           3 mann keine abg. Ausbild. 2 hi-unemp
           4 mann keine abg. Ausbild. 2 lo-unemp
           5 mann keine abg. Ausbild. 3 hi-unemp
           6 mann keine abg. Ausbild. 3 lo-unemp
           7 mann abg. Ausbildung 1 hi-unemp
           8 mann abg. Ausbildung 1 lo-unemp
           9 mann abg. Ausbildung 2 hi-unemp
          10 mann abg. Ausbildung 2 lo-unemp
          11 mann abg. Ausbildung 3 hi-unemp
          12 mann abg. Ausbildung 3 lo-unemp
          13 frau keine abg. Ausbild. 1 hi-unemp
          14 frau keine abg. Ausbild. 1 lo-unemp
          15 frau keine abg. Ausbild. 2 hi-unemp
          16 frau keine abg. Ausbild. 2 lo-unemp
          17 frau keine abg. Ausbild. 3 hi-unemp
          18 frau keine abg. Ausbild. 3 lo-unemp
          19 frau abg. Ausbildung 1 hi-unemp
          20 frau abg. Ausbildung 1 lo-unemp
          21 frau abg. Ausbildung 2 hi-unemp
          22 frau abg. Ausbildung 2 lo-unemp
          23 frau abg. Ausbildung 3 hi-unemp
          24 frau abg. Ausbildung 3 lo-unemp
r; t=0.00 14:52:24

.                         tab cell, matcell(spellcounts) matrow(matname)

    group(frau educ2 agecat2 macro) |      Freq.     Percent        Cum.
------------------------------------+-----------------------------------
mann keine abg. Ausbild. 1 hi-unemp |     53,823        3.97        3.97
mann keine abg. Ausbild. 1 lo-unemp |     33,632        2.48        6.44
mann keine abg. Ausbild. 2 hi-unemp |     12,740        0.94        7.38
mann keine abg. Ausbild. 2 lo-unemp |     12,511        0.92        8.30
mann keine abg. Ausbild. 3 hi-unemp |     14,615        1.08        9.38
mann keine abg. Ausbild. 3 lo-unemp |     16,349        1.20       10.58
    mann abg. Ausbildung 1 hi-unemp |     86,058        6.34       16.92
    mann abg. Ausbildung 1 lo-unemp |    103,715        7.64       24.57
    mann abg. Ausbildung 2 hi-unemp |    184,685       13.61       38.17
    mann abg. Ausbildung 2 lo-unemp |    230,175       16.96       55.13
    mann abg. Ausbildung 3 hi-unemp |    126,210        9.30       64.43
    mann abg. Ausbildung 3 lo-unemp |    188,379       13.88       78.31
frau keine abg. Ausbild. 1 hi-unemp |      9,778        0.72       79.03
frau keine abg. Ausbild. 1 lo-unemp |      5,796        0.43       79.45
frau keine abg. Ausbild. 2 hi-unemp |      3,020        0.22       79.68
frau keine abg. Ausbild. 2 lo-unemp |      3,490        0.26       79.93
frau keine abg. Ausbild. 3 hi-unemp |      2,870        0.21       80.15
frau keine abg. Ausbild. 3 lo-unemp |      3,756        0.28       80.42
    frau abg. Ausbildung 1 hi-unemp |     21,731        1.60       82.02
    frau abg. Ausbildung 1 lo-unemp |     23,140        1.70       83.73
    frau abg. Ausbildung 2 hi-unemp |     60,429        4.45       88.18
    frau abg. Ausbildung 2 lo-unemp |     71,880        5.30       93.48
    frau abg. Ausbildung 3 hi-unemp |     33,878        2.50       95.97
    frau abg. Ausbildung 3 lo-unemp |     54,670        4.03      100.00
------------------------------------+-----------------------------------
                              Total |  1,357,330      100.00
r; t=0.30 14:52:24

.                         putexcel set results/${samplefolder}/5_sample${sample}_cells_stats.xlsx, sheet("nb_spells") modify
r; t=0.04 14:52:24

.                         putexcel B1=("nb spells") A2= matrix(matname) B2=matrix(spellcounts)
file results/two/5_sample2_cells_stats.xlsx saved
r; t=0.07 14:52:24

.                         `putexcelclose'
r; t=0.00 14:52:24

. 
.                         *7bi) Number of distinct persons in cells
.                         cap drop dummy infopers countinfopers
r; t=0.00 14:52:24

.                         gen dummy=1 
r; t=0.05 14:52:24

.                         bys persnr cell: egen infopers=max(dummy)
r; t=1.38 14:52:26

.                         bys persnr cell: gen countinfopers=1 if (infopers==1 & _n==1)
(938,548 missing values generated)
r; t=0.09 14:52:26

.                         estpost tabstat countinfopers, by(cell)  statistics(count) columns(statistics)

Summary statistics: count
     for variables: countinfopers
  by categories of: cell

        cell |  e(count) 
-------------+-----------
           1 |     18101 
           2 |     10671 
           3 |      6627 
           4 |      4709 
           5 |      8361 
           6 |      7544 
           7 |     27664 
           8 |     27732 
           9 |     55114 
          10 |     51947 
          11 |     52358 
          12 |     65515 
          13 |      3244 
          14 |      1857 
          15 |      1529 
          16 |      1383 
          17 |      1578 
          18 |      1618 
          19 |      7975 
          20 |      7168 
          21 |     17868 
          22 |     17249 
          23 |     14319 
          24 |     19337 
-------------+-----------
       Total |    431468 

category labels saved in macro e(labels)
r; t=9.64 14:52:36

.                         putexcel set results/${samplefolder}/5_sample${sample}_cells_stats.xlsx, sheet("nb_distinctpersons") modify
r; t=0.01 14:52:36

.                         putexcel B1=("nb distinct persons") A2= matrix(matname) B2=matrix(e(count)')
file results/two/5_sample2_cells_stats.xlsx saved
r; t=0.06 14:52:36

.                         `putexcelclose'
r; t=0.00 14:52:36

.                         drop dummy infopers countinfopers
r; t=0.00 14:52:36

.                         
.                         *7bii) Number of distinct persons in lignite coal in 2017
.                         * Method 1
.                         tab cell if begepi<mdy(6,1,2017) & endepi>mdy(6,1,2017) & thisspelllignite==1 & statsimple==1, matrow(matname)

    group(frau educ2 agecat2 macro) |      Freq.     Percent        Cum.
------------------------------------+-----------------------------------
mann keine abg. Ausbild. 1 lo-unemp |        479        4.12        4.12
mann keine abg. Ausbild. 2 lo-unemp |        332        2.86        6.98
mann keine abg. Ausbild. 3 lo-unemp |        911        7.84       14.81
    mann abg. Ausbildung 1 lo-unemp |      1,088        9.36       24.17
    mann abg. Ausbildung 2 lo-unemp |      2,158       18.56       42.73
    mann abg. Ausbildung 3 lo-unemp |      5,365       46.15       88.88
frau keine abg. Ausbild. 1 lo-unemp |         NA        0.40       89.27
frau keine abg. Ausbild. 2 lo-unemp |         NA        0.16       89.44
frau keine abg. Ausbild. 3 lo-unemp |         NA        0.20       89.64
    frau abg. Ausbildung 1 lo-unemp |        163        1.40       91.04
    frau abg. Ausbildung 2 lo-unemp |        378        3.25       94.29
    frau abg. Ausbildung 3 lo-unemp |        664        5.71      100.00
------------------------------------+-----------------------------------
                              Total |     11,626      100.00
r; t=0.74 14:52:36

.                         capture noisily estpost tab cell if begepi<mdy(6,1,2017) & endepi>mdy(6,1,2017) & thisspelllignite==1 & statsimple==1

        cell |      e(b)     e(pct)  e(cumpct) 
-------------+---------------------------------
           2 |       479   4.120076   4.120076 
           4 |       332   2.855668   6.975744 
           6 |       911   7.835885   14.81163 
           8 |      1088   9.358335   24.16996 
          10 |      2158   18.56184   42.73181 
          12 |      5365   46.14657   88.87838 
          14 |        NA   .3956649   89.27404 
          16 |        NA   .1634268   89.43747 
          18 |        NA   .1978324    89.6353 
          20 |       163    1.40203   91.03733 
          22 |       378   3.251333   94.28866 
          24 |       664   5.711337        100 
-------------+---------------------------------
       Total |     11626        100            

row labels saved in macro e(labels)
r; t=0.42 14:52:37

.                         if _rc!=2000{
.                                 putexcel set results/${samplefolder}/5_sample${sample}_cells_stats, sheet("nb_distinctpersons_coal2017") modify
r; t=0.01 14:52:37
.                                 putexcel B1=("nb distinct persons in coal in 2017 (method 1)") A2=matrix(matname) B2=matrix(e(b)')
file results/two/5_sample2_cells_stats.xlsx saved
r; t=0.07 14:52:37
.                                 `putexcelclose'
r; t=0.00 14:52:37
.                         }
r; t=0.08 14:52:37

.                         *Method 2
.                         cap drop dummy infopers countinfopers
r; t=0.00 14:52:37

.                         gen dummy=1 if begepi<mdy(6,1,2017) & endepi>mdy(6,1,2017) & thisspelllignite==1 & statsimple==1 
(1,370,102 missing values generated)
r; t=0.13 14:52:37

.                         bys persnr cell: egen infopers=max(dummy)
(1,359,755 missing values generated)
r; t=0.77 14:52:38

.                         bys persnr cell: gen countinfopers=1 if (infopers==1 & _n==1)
(1,370,102 missing values generated)
r; t=0.08 14:52:38

.                         capture noisily estpost tabstat countinfopers, by(cell)  statistics(count) columns(statistics)

Summary statistics: count
     for variables: countinfopers
  by categories of: cell

        cell |  e(count) 
-------------+-----------
           2 |       479 
           4 |       332 
           6 |       911 
           8 |      1088 
          10 |      2158 
          12 |      5365 
          14 |        NA 
          16 |        NA 
          18 |        NA 
          20 |       163 
          22 |       378 
          24 |       664 
-------------+-----------
       Total |     11626 

category labels saved in macro e(labels)
r; t=6.51 14:52:44

.                         if _rc!=2000{
.                         putexcel set results/${samplefolder}/5_sample${sample}_cells_stats.xlsx, sheet("nb_distinctpersons_coal2017") modify
r; t=0.01 14:52:44
.                         putexcel C1=("nb distinct persons in coal in 2017 (method 2)") C2=matrix(e(count)')
file results/two/5_sample2_cells_stats.xlsx saved
r; t=0.06 14:52:44
.                         `putexcelclose'
r; t=0.00 14:52:44
.                         }
r; t=0.08 14:52:44

.                         drop dummy infopers countinfopers
r; t=0.00 14:52:44

.                         
.                         *7c) Number of TRANSITIONS in cells 
. *                       - transition to retirement (pretrans = 1 | pretrans = 6 )
. *                       - transition to unemployment (pretrans = 7)
. *                       - transition from unemployment to job (pretrans = 8)                            */
. 
.                         di "transitions: number of observations: education, gender, age (stata) "
transitions: number of observations: education, gender, age (stata) 
r; t=0.00 14:52:45

.                         foreach i in 1 6 7 8 11 12 {
  2.                                 tab cell if pretrans == `i', matcell(spellcounts_`i') matrow(matname)
  3.                                 putexcel set results/${samplefolder}/5_sample${sample}_cells_stats.xlsx, sheet("nb_transitions`i'") modify
  4.                                 putexcel B1=("nb transitions`i'") A2= matrix(matname) B2=matrix(spellcounts_`i')
  5.                                 `putexcelclose'
  6.                         }

    group(frau educ2 agecat2 macro) |      Freq.     Percent        Cum.
------------------------------------+-----------------------------------
mann keine abg. Ausbild. 2 hi-unemp |         NA        0.09        0.09
mann keine abg. Ausbild. 3 hi-unemp |      2,401       24.19       24.28
mann keine abg. Ausbild. 3 lo-unemp |        380        3.83       28.11
    mann abg. Ausbildung 2 hi-unemp |         NA        0.27       28.38
    mann abg. Ausbildung 3 hi-unemp |      5,076       51.13       79.51
    mann abg. Ausbildung 3 lo-unemp |      1,260       12.69       92.20
frau keine abg. Ausbild. 2 hi-unemp |         NA        0.01       92.21
frau keine abg. Ausbild. 3 hi-unemp |        222        2.24       94.45
frau keine abg. Ausbild. 3 lo-unemp |         NA        0.29       94.74
    frau abg. Ausbildung 2 hi-unemp |         NA        0.03       94.77
    frau abg. Ausbildung 3 hi-unemp |        367        3.70       98.47
    frau abg. Ausbildung 3 lo-unemp |        152        1.53      100.00
------------------------------------+-----------------------------------
                              Total |      9,927      100.00
file results/two/5_sample2_cells_stats.xlsx saved

    group(frau educ2 agecat2 macro) |      Freq.     Percent        Cum.
------------------------------------+-----------------------------------
mann keine abg. Ausbild. 2 hi-unemp |         NA        0.11        0.11
mann keine abg. Ausbild. 2 lo-unemp |         NA        0.03        0.14
mann keine abg. Ausbild. 3 hi-unemp |      1,563       11.20       11.33
mann keine abg. Ausbild. 3 lo-unemp |        130        0.93       12.26
    mann abg. Ausbildung 2 hi-unemp |        107        0.77       13.03
    mann abg. Ausbildung 2 lo-unemp |         NA        0.04       13.07
    mann abg. Ausbildung 3 hi-unemp |      9,095       65.15       78.22
    mann abg. Ausbildung 3 lo-unemp |        273        1.96       80.17
frau keine abg. Ausbild. 2 hi-unemp |         NA        0.29       80.47
frau keine abg. Ausbild. 3 hi-unemp |        408        2.92       83.39
frau keine abg. Ausbild. 3 lo-unemp |         NA        0.07       83.46
    frau abg. Ausbildung 2 hi-unemp |        137        0.98       84.44
    frau abg. Ausbildung 3 hi-unemp |      2,152       15.42       99.86
    frau abg. Ausbildung 3 lo-unemp |         NA        0.14      100.00
------------------------------------+-----------------------------------
                              Total |     13,960      100.00
file results/two/5_sample2_cells_stats.xlsx saved

    group(frau educ2 agecat2 macro) |      Freq.     Percent        Cum.
------------------------------------+-----------------------------------
mann keine abg. Ausbild. 1 hi-unemp |        647        2.90        2.90
mann keine abg. Ausbild. 1 lo-unemp |        110        0.49        3.40
mann keine abg. Ausbild. 2 hi-unemp |        484        2.17        5.57
mann keine abg. Ausbild. 2 lo-unemp |         NA        0.44        6.01
mann keine abg. Ausbild. 3 hi-unemp |        115        0.52        6.53
mann keine abg. Ausbild. 3 lo-unemp |         NA        0.18        6.71
    mann abg. Ausbildung 1 hi-unemp |      3,851       17.28       24.00
    mann abg. Ausbildung 1 lo-unemp |        197        0.88       24.88
    mann abg. Ausbildung 2 hi-unemp |      7,267       32.62       57.50
    mann abg. Ausbildung 2 lo-unemp |        264        1.18       58.68
    mann abg. Ausbildung 3 hi-unemp |      1,982        8.90       67.58
    mann abg. Ausbildung 3 lo-unemp |        133        0.60       68.18
frau keine abg. Ausbild. 1 hi-unemp |        247        1.11       69.29
frau keine abg. Ausbild. 1 lo-unemp |         NA        0.02       69.31
frau keine abg. Ausbild. 2 hi-unemp |        374        1.68       70.99
frau keine abg. Ausbild. 2 lo-unemp |         NA        0.03       71.01
frau keine abg. Ausbild. 3 hi-unemp |        121        0.54       71.56
frau keine abg. Ausbild. 3 lo-unemp |         NA        0.00       71.56
    frau abg. Ausbildung 1 hi-unemp |      1,838        8.25       79.81
    frau abg. Ausbildung 1 lo-unemp |         NA        0.15       79.96
    frau abg. Ausbildung 2 hi-unemp |      3,801       17.06       97.02
    frau abg. Ausbildung 2 lo-unemp |         NA        0.06       97.08
    frau abg. Ausbildung 3 hi-unemp |        642        2.88       99.96
    frau abg. Ausbildung 3 lo-unemp |         NA        0.04      100.00
------------------------------------+-----------------------------------
                              Total |     22,280      100.00
file results/two/5_sample2_cells_stats.xlsx saved

    group(frau educ2 agecat2 macro) |      Freq.     Percent        Cum.
------------------------------------+-----------------------------------
mann keine abg. Ausbild. 1 hi-unemp |         NA        1.49        1.49
mann keine abg. Ausbild. 1 lo-unemp |         NA        0.18        1.67
mann keine abg. Ausbild. 2 hi-unemp |         NA        2.41        4.08
mann keine abg. Ausbild. 2 lo-unemp |         NA        0.15        4.23
mann keine abg. Ausbild. 3 hi-unemp |         NA        1.18        5.41
mann keine abg. Ausbild. 3 lo-unemp |         NA        0.38        5.80
    mann abg. Ausbildung 1 hi-unemp |        490       12.57       18.37
    mann abg. Ausbildung 1 lo-unemp |         NA        0.36       18.73
    mann abg. Ausbildung 2 hi-unemp |      1,388       35.62       54.35
    mann abg. Ausbildung 2 lo-unemp |         NA        0.74       55.09
    mann abg. Ausbildung 3 hi-unemp |        810       20.79       75.88
    mann abg. Ausbildung 3 lo-unemp |         NA        1.18       77.06
frau keine abg. Ausbild. 1 hi-unemp |         NA        0.62       77.68
frau keine abg. Ausbild. 1 lo-unemp |         NA        0.03       77.70
frau keine abg. Ausbild. 2 hi-unemp |         NA        1.49       79.19
frau keine abg. Ausbild. 2 lo-unemp |         NA        0.62       79.80
frau keine abg. Ausbild. 3 hi-unemp |         NA        0.62       80.42
frau keine abg. Ausbild. 3 lo-unemp |         NA        0.44       80.86
    frau abg. Ausbildung 1 hi-unemp |        180        4.62       85.48
    frau abg. Ausbildung 1 lo-unemp |         NA        0.13       85.60
    frau abg. Ausbildung 2 hi-unemp |        423       10.85       96.46
    frau abg. Ausbildung 2 lo-unemp |         NA        0.33       96.79
    frau abg. Ausbildung 3 hi-unemp |        117        3.00       99.79
    frau abg. Ausbildung 3 lo-unemp |         NA        0.21      100.00
------------------------------------+-----------------------------------
                              Total |      3,897      100.00
file results/two/5_sample2_cells_stats.xlsx saved

    group(frau educ2 agecat2 macro) |      Freq.     Percent        Cum.
------------------------------------+-----------------------------------
mann keine abg. Ausbild. 1 hi-unemp |      1,036        0.60        0.60
mann keine abg. Ausbild. 1 lo-unemp |        373        0.21        0.81
mann keine abg. Ausbild. 2 hi-unemp |      1,019        0.59        1.40
mann keine abg. Ausbild. 2 lo-unemp |        417        0.24        1.64
mann keine abg. Ausbild. 3 hi-unemp |        230        0.13        1.77
mann keine abg. Ausbild. 3 lo-unemp |        189        0.11        1.88
    mann abg. Ausbildung 1 hi-unemp |     19,148       11.03       12.91
    mann abg. Ausbildung 1 lo-unemp |      5,464        3.15       16.06
    mann abg. Ausbildung 2 hi-unemp |     64,040       36.89       52.95
    mann abg. Ausbildung 2 lo-unemp |     16,032        9.24       62.18
    mann abg. Ausbildung 3 hi-unemp |     22,108       12.74       74.92
    mann abg. Ausbildung 3 lo-unemp |      6,878        3.96       78.88
frau keine abg. Ausbild. 1 hi-unemp |        176        0.10       78.98
frau keine abg. Ausbild. 1 lo-unemp |         NA        0.04       79.02
frau keine abg. Ausbild. 2 hi-unemp |        522        0.30       79.33
frau keine abg. Ausbild. 2 lo-unemp |         NA        0.04       79.37
frau keine abg. Ausbild. 3 hi-unemp |        210        0.12       79.49
frau keine abg. Ausbild. 3 lo-unemp |         NA        0.03       79.52
    frau abg. Ausbildung 1 hi-unemp |      3,797        2.19       81.71
    frau abg. Ausbildung 1 lo-unemp |        896        0.52       82.22
    frau abg. Ausbildung 2 hi-unemp |     19,894       11.46       93.68
    frau abg. Ausbildung 2 lo-unemp |      3,077        1.77       95.46
    frau abg. Ausbildung 3 hi-unemp |      6,193        3.57       99.02
    frau abg. Ausbildung 3 lo-unemp |      1,697        0.98      100.00
------------------------------------+-----------------------------------
                              Total |    173,599      100.00
file results/two/5_sample2_cells_stats.xlsx saved

    group(frau educ2 agecat2 macro) |      Freq.     Percent        Cum.
------------------------------------+-----------------------------------
mann keine abg. Ausbild. 1 hi-unemp |        110        2.44        2.44
mann keine abg. Ausbild. 1 lo-unemp |         NA        0.49        2.93
mann keine abg. Ausbild. 2 hi-unemp |         NA        1.68        4.61
mann keine abg. Ausbild. 2 lo-unemp |         NA        0.24        4.85
mann keine abg. Ausbild. 3 hi-unemp |         NA        0.49        5.34
mann keine abg. Ausbild. 3 lo-unemp |         NA        0.04        5.39
    mann abg. Ausbildung 1 hi-unemp |        791       17.53       22.92
    mann abg. Ausbildung 1 lo-unemp |        188        4.17       27.08
    mann abg. Ausbildung 2 hi-unemp |      1,662       36.84       63.92
    mann abg. Ausbildung 2 lo-unemp |        257        5.70       69.61
    mann abg. Ausbildung 3 hi-unemp |        733       16.25       85.86
    mann abg. Ausbildung 3 lo-unemp |         NA        1.37       87.23
frau keine abg. Ausbild. 1 hi-unemp |         NA        0.27       87.50
frau keine abg. Ausbild. 1 lo-unemp |         NA        0.07       87.57
frau keine abg. Ausbild. 2 hi-unemp |         NA        0.51       88.08
frau keine abg. Ausbild. 3 hi-unemp |         NA        0.02       88.10
    frau abg. Ausbildung 1 hi-unemp |        143        3.17       91.27
    frau abg. Ausbildung 1 lo-unemp |         NA        0.71       91.98
    frau abg. Ausbildung 2 hi-unemp |        275        6.09       98.07
    frau abg. Ausbildung 2 lo-unemp |         NA        0.71       98.78
    frau abg. Ausbildung 3 hi-unemp |         NA        1.00       99.78
    frau abg. Ausbildung 3 lo-unemp |         NA        0.22      100.00
------------------------------------+-----------------------------------
                              Total |      4,512      100.00
file results/two/5_sample2_cells_stats.xlsx saved
r; t=2.07 14:52:47

. 
. 
. /* ------------------------------------------------------------------------ */
.  *      SAVE AFTER CUTTING SPELLS AND CREATING CELLS -> POSTCOLL_CELL.DTA
. /* ------------------------------------------------------------------------ */  
. save ${data}\postcoll_cell.dta, replace
file \\iab.baintern.de\DFS\017\Ablagen\D01700-Projekte\D01700-COAL\data\postcoll_cell.dta saved
r; t=46.17 14:53:33

. 
. /* ------------------------------------------------------------------------ */
.  *      BASIC DESCRIPTIVE STATISTICS ON CHARACTERISTICS AFTER CUTTING SPELLS
. /* ------------------------------------------------------------------------ */
. tab ageend, m

 age at end |
   of spell |      Freq.     Percent        Cum.
------------+-----------------------------------
         14 |         NA        0.00        0.00
         15 |         NA        0.00        0.00
         16 |         NA        0.00        0.00
         17 |         NA        0.00        0.00
         18 |     21,792        1.58        1.58
         19 |     28,342        2.05        3.63
         20 |     34,091        2.47        6.10
         21 |     35,049        2.54        8.63
         22 |     29,790        2.15       10.79
         23 |     26,373        1.91       12.69
         24 |     24,345        1.76       14.45
         25 |     24,014        1.74       16.19
         26 |     24,030        1.74       17.93
         27 |     24,139        1.75       19.68
         28 |     23,927        1.73       21.41
         29 |     24,395        1.76       23.17
         30 |     24,879        1.80       24.97
         31 |     25,270        1.83       26.80
         32 |     26,277        1.90       28.70
         33 |     27,007        1.95       30.65
         34 |     27,609        2.00       32.65
         35 |     28,398        2.05       34.71
         36 |     29,080        2.10       36.81
         37 |     29,459        2.13       38.94
         38 |     29,987        2.17       41.11
         39 |     30,429        2.20       43.31
         40 |     31,325        2.27       45.58
         41 |     32,216        2.33       47.91
         42 |     32,560        2.36       50.26
         43 |     33,018        2.39       52.65
         44 |     33,459        2.42       55.07
         45 |     33,615        2.43       57.50
         46 |     33,433        2.42       59.92
         47 |     33,030        2.39       62.31
         48 |     33,424        2.42       64.73
         49 |     33,258        2.41       67.13
         50 |     33,624        2.43       69.57
         51 |     35,051        2.54       72.10
         52 |     34,613        2.50       74.61
         53 |     34,650        2.51       77.11
         54 |     35,334        2.56       79.67
         55 |     41,053        2.97       82.64
         56 |     35,489        2.57       85.21
         57 |     32,736        2.37       87.57
         58 |     31,171        2.25       89.83
         59 |     26,222        1.90       91.72
         60 |     43,480        3.15       94.87
         61 |     17,831        1.29       96.16
         62 |     13,212        0.96       97.12
         63 |     14,082        1.02       98.13
         64 |      6,522        0.47       98.61
         65 |      5,576        0.40       99.01
         66 |      3,237        0.23       99.24
         67 |      2,389        0.17       99.42
         68 |      1,818        0.13       99.55
         69 |      1,508        0.11       99.66
         70 |      1,196        0.09       99.74
         71 |        933        0.07       99.81
         72 |        775        0.06       99.87
         73 |        640        0.05       99.91
         74 |        449        0.03       99.95
         75 |        339        0.02       99.97
         76 |        406        0.03      100.00
------------+-----------------------------------
      Total |  1,382,388      100.00
r; t=0.48 14:53:33

. tab agecat2, m          

    agecat2 |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |    345,166       24.97       24.97
          2 |    582,854       42.16       67.13
          3 |    454,336       32.87      100.00
          . |         NA        0.00      100.00
------------+-----------------------------------
      Total |  1,382,388      100.00
r; t=0.27 14:53:34

. tab frau, m

       frau |      Freq.     Percent        Cum.
------------+-----------------------------------
       mann |  1,080,157       78.14       78.14
       frau |    302,231       21.86      100.00
------------+-----------------------------------
      Total |  1,382,388      100.00
r; t=0.25 14:53:34

. tab educ2, m

        education 2 |
         categories |      Freq.     Percent        Cum.
--------------------+-----------------------------------
keine abg. Ausbild. |    172,412       12.47       12.47
    abg. Ausbildung |  1,184,950       85.72       98.19
                  . |     25,026        1.81      100.00
--------------------+-----------------------------------
              Total |  1,382,388      100.00
r; t=0.26 14:53:34

. tab mining_area, m      

       mining_area |      Freq.     Percent        Cum.
-------------------+-----------------------------------
  Lausitzer Revier |    264,835       19.16       19.16
  Mitteldt. Revier |    184,567       13.35       32.51
Helmstedter Revier |     23,084        1.67       34.18
Rheinisches Revier |    165,635       11.98       46.16
     Other Reviere |     38,754        2.80       48.96
                 . |    705,513       51.04      100.00
-------------------+-----------------------------------
             Total |  1,382,388      100.00
r; t=0.29 14:53:34

. tab macro, m

      macro |      Freq.     Percent        Cum.
------------+-----------------------------------
   hi-unemp |    623,568       45.11       45.11
   lo-unemp |    758,820       54.89      100.00
------------+-----------------------------------
      Total |  1,382,388      100.00
r; t=0.29 14:53:35

. *We want to know if we have black hole spells in the sample (statsimple=.)
. tab statsimple,m        

        0 - |
unemployed, |
 margemp or |
 ALMP / 1 - |
 employed / |
        2 - |
 vocational |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |    420,621       30.43       30.43
          1 |    654,230       47.33       77.75
          2 |     97,606        7.06       84.81
          . |    209,931       15.19      100.00
------------+-----------------------------------
      Total |  1,382,388      100.00
r; t=0.27 14:53:35

. 
. /* ------------------------------------------------------------------------ */
.  *  (8) Estimate transition parameters by cell (wage parameters in part 9)
. /* ------------------------------------------------------------------------ */                  
. /*      8.a - rho (ex mu): retirement
>         8.b - deltalig, deltanonlig
>         8.c - lambdalig, lambdanonlig, lambdazerolig
> 
>         ASSUMPTION : constant risk over time    
>         CENSORING : waiting time > observation time
>         METHOD :        we use the maximum likelihod estimate of the hazard =  [total number of transition / total exposure time]
> */
.         
.         /* Different possibilities of scenarios in the loop to test excluding outliers
>                 - 0 : use whole sample
>                 - 0.01 : exclude bottom and top outliers of the duration distribution at percentile 0.01
>                 - 1 : exclude bottom and top outliers of the duration distribution at percentile 1
>                 - 2 : exclude bottom and top outliers of the duration distribution at percentile 2
>                 - 3 : excluding 1 day duration spells
>                 - 4 : excluding 1 to 6 days duration spells
>                 - 5 : excluding 1% top duration spells
>                 - 6 : excluding 2% top duration spells
>                 - 7 : excluding all observations of people who have at least 1 day spell
>         */
.                 
.         foreach x in 0 /*0.01 0.1 1 2 3 4 5 6 7*/ {
  2.         
.         use ${data}\postcoll_cell.dta, clear
  3.         
.         cap drop keep
  4.         gen keep=1
  5. 
.         * Whole sample
.         if  `x'==0 {
  6.         di "Analysis on the whole sample"
  7.         local folder="all"
  8.         }
  9.                 
.         * Removing spells at top and bottom percentile of distribution  
.         if `x'>0 & `x'<3 {
 10.         di "Analysis excluding bottom and top outliers of the duration distribution at the p`x'"        
 11.         if `x'==0.01{ 
 12.                         local folder="P001"
 13.         }
 14.         if `x'==0.1{ 
 15.         local folder="P01"
 16.         }
 17.         if `x'==1{ 
 18.                 local folder="P1"
 19.         }
 20.         if `x'==2{ 
 21.                 local folder="P2"
 22.         }
 23.         local top= 100-`x'
 24.         _pctile dur, p(`x' `top')
 25.         return list
 26.         local threshold_bottom = `r(r1)'        
 27.         local threshold_top = `r(r2)'   
 28.         replace keep=0 if dur<`threshold_bottom' | dur>`threshold_top'
 29.         }
 30.         
.         * Removing 1 day duration spells
.         if `x'==3 {
 31.         di "Analysis excluding 1 day duration spells"
 32.         local folder="Without1day"
 33.         replace keep=0 if dur==1
 34.         }       
 35.         
.         * Removing  1 to 6 day duration spells
.         if `x'==4 {
 36.         di "Analysis excluding 1 to 6 day duration spells"
 37.         local folder="Without1to6day"
 38.         replace keep=0 if dur<7
 39.         }       
 40.         
.         * Removing 1% top distribution spells
.         if `x'==5 {
 41.         di "Analysis excluding 1% top distribution spells"
 42.         local folder="WithoutP1Top"     
 43.         _pctile dur, p(99)
 44.         return list
 45.         local threshold_top = `r(r1)'   
 46.         replace keep=0 if dur>`threshold_top'
 47.         }
 48.         
.         * Removing 2% top distribution spells
.         if `x'==6 {
 49.         di "Analysis excluding 2% top distribution spells"
 50.         local folder="WithoutP2Top"             
 51.         _pctile dur, p(98)
 52.         return list
 53.         local threshold_top = `r(r1)'   
 54.         replace keep=0 if dur>`threshold_top'
 55.         }
 56.         
.         * Removing all observations of people who have at least 1 day spell
.         if `x'==7 {
 57.         di "Analysis excluding all observations of people who have at least 1 day spell"
 58.         local folder="WithoutPersnr1day"                
 59.         cap drop shortdur
 60.         gen shortdur=0
 61.         bysort persnr (begepi): replace shortdur=1 if dur==1
 62.         tab shortdur
 63.         *copy values to all observations
.         cap drop indivshort
 64.         bysort persnr (begepi): egen indivshort=max(shortdur)
 65.         tab indivshort
 66.         replace keep=0 if indivshort==1
 67.         }
 68.         
.         tab keep
 69.         sum dur if keep==0, detail
 70.         tab pretrans keep
 71.         tab posttrans keep      
 72.         drop if keep==0 
 73.         sum dur, detail
 74.         sum durrisk, detail
 75.         
.         cap mkdir results/${samplefolder}/`folder'
 76.         
.         /* -------------------------------------------- */
.          *  (8a)  Retirement probability rho (ex-mu)
.         /* -------------------------------------------- */
.         *       We estimate three different rhos (ex-mu's:)
.         *       1) do not distinguish between those two types of retirement
.         *       2) rho (ex-mu) direct retirement probability  (pretrans == 1)   -> lignite -> retirement
.         *       3) rho (ex-mu) indirect retirement probability (pretrans == 6)  -> lignite -> unemp/marg emp/ALMP -> retirement
. 
.         * by cells
.         di "Lignite leavers' Retirement probability (according to cell)"
 77. 
.         * Number of distinct persons at risk                    
.         cap drop dummy infopers countinfopers
 78.         gen dummy=0
 79.         replace dummy=1 if Emp_Lig==1 & durretrisk!=0
 80.         bys persnr cell: egen infopers=max(dummy)
 81.         bys persnr cell: gen countinfopers=1 if (infopers==1 & _n==1)
 82.                         
.         forvalues i = 1/$cellnumber {
 83.                 local j=`i'+2
 84.                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("rho") modify
 85.                 putexcel A`j'=("`i'")
 86.                 `putexcelclose'
 87.                 * Number of distinct persons at risk
.                 di "distinct cell `i'"
 88.                 capture noisily estpost sum countinfopers if cell == `i'
 89.                 if _rc!=2000{
 90.                         putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("rho") modify
 91.                         putexcel D1=("nb distinct persons at risk") D`j'=matrix(e(count))
 92.                         `putexcelclose'
 93.                 }
 94.                 
.                 * Exposure time : time of employment in lignite (Emp_Lig1==1)
.                 di "durretrisk cell `i'"
 95.                 capture noisily estpost sum durretrisk if Emp_Lig==1 & cell == `i'
 96.                 if _rc!=2000{
 97.                         putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("rho") modify
 98.                         putexcel E1=("time at risk") E`j'=matrix(e(sum))
 99.                         `putexcelclose'
100.                 }
101.                 
.                 *rho (ex-mu)
.                 * Transitions : not right-censored (end==1) and pretrans==1 or 6
.                 di "transition cell `i'"
102.                 capture noisily estpost sum end if (pretrans==1 | pretrans==6) & cell == `i'
103.                 if _rc!=2000{
104.                         putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("rho") modify
105.                         putexcel C1=("transitions") C`j'=matrix(e(sum))
106.                         `putexcelclose' 
107.                         if ${iab}==1{
108.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("rho") modify
109.                                 putexcel B1=("rho") B`j'=formula(=C`j'/E`j')
110.                                 `putexcelclose'
111.                         }
112.                 }
113.  }
114. 
. 
. *Age distribution in lignite in 2017 by cells
. cap drop presentcoal2017 agecoal2017
115. gen presentcoal2017=0
116. replace presentcoal2017=1 if thisspelllignite==1 & begepi<mdy(6,1,2017) & endepi>mdy(6,1,2017) 
117. gen agecoal2017=2017-year(geb_dat) if presentcoal2017==1 
118. 
. * we create a sheet with age in row and cell in columns
. * we thus want to assign cell number 1 in column B, 
. *  cell number 2 in column C, etc.
. * for this we attribute a number for each letter in the alphabet: 
. * we use `c(ALPHA)' which returns a string containing "A B C ... Z" 
. * and tokenize which divides string into tokens
. tokenize "`c(ALPHA)'"
119. * we create a local variable k =2 which is incremented by 1 
. * at each loop on cells, 
. * and use ``k'' to return the letter corresponding to position k in alphabet 
. local k=2
120. forvalues i = 1/$cellnumber {
121.         forvalues a = 18/65{
122.                 local r=`a'-16
123.                 capture noisily count if agecoal2017==`a' & cell==`i'
124.                 if _rc!=2000{
125.                         putexcel set results/${samplefolder}/5_age, sheet("distrib_age_coal2017") modify
126.                         putexcel A`r'=("`a'") ``k''1=("cell`i'") ``k''`r'=(r(N))
127.                         `putexcelclose'
128.                 }
129.         }
130.         local ++k
131. }
132.                         
. **************************************
. *** rho by age 50-65, whole sample ***
. **************************************
.         forvalues j = 50/65 {
133.                 local r=`j'-48
134.                 * rho now not by cells, but by specific ages 
.                 * Exposure time : time of employment in lignite (Emp_Lig1==1)
.                 di "durretrisk age `j'"
135.                 capture noisily estpost sum durretrisk if Emp_Lig==1 & ageend == `j'
136.                 if _rc!=2000{
137.                         putexcel set results/${samplefolder}/5_age, sheet("rho_wholesample") modify
138.                         putexcel A`r'=("`j'") D1=("time at risk") D`r'=matrix(e(sum))
139.                         `putexcelclose'
140.                 }
141.  
.                 * Transitions : not right-censored (end==1) and pretrans==1 or 6
.                 di "transition age `i'"
142.                 capture noisily estpost sum end if (pretrans==1 | pretrans==6) & ageend == `j'
143.                 if _rc!=2000{
144.                         putexcel set results/${samplefolder}/5_age, sheet("rho_wholesample") modify
145.                         putexcel C1=("transitions") C`r'=matrix(e(sum))
146.                         `putexcelclose' 
147.                         if ${iab}==1{
148.                         putexcel set results/${samplefolder}/5_age, sheet("rho_wholesample") modify
149.                         putexcel B1=("rho") B`r'=formula(=C`r'/D`r')
150.                         `putexcelclose'
151.                         }
152.                 }
153.         }
154.         
. *******************************************************
. *** rho by age 50-65, by cell (if cells incl. ages) ***
. *******************************************************
. 
. if ${sample}<7{
155. * START: 
. * skip this part if cells include no age-specific categories:
. * in benchmark, we have age-specific categories, in JAERE-requested
. * samples 7, 8, and 9, we have no age-specific categories
. 
. cap drop cellbis cellret
156. egen cellbis=group(frau educ2 macro), label
157. tab cellbis, m
158. gen cellret=.
159. replace cellret=5 if cellbis==1
160. replace cellret=6 if cellbis==2
161. replace cellret=11 if cellbis==3
162. replace cellret=12 if cellbis==4
163. replace cellret=17 if cellbis==5
164. replace cellret=18 if cellbis==6
165. replace cellret=23 if cellbis==7
166. replace cellret=24 if cellbis==8
167. tab cellret, m
168. global cellretnumber "5 6 11 12 17 18 23 24"
169. 
. foreach c of global cellretnumber {
170.         forvalues j = 50/65 {
171.                 local r=`j'-48
172.                 di "durretrisk cell`c' age `j'"         
173.                 * Exposure time : time of employment in lignite (Emp_Lig==1)
.                 di "durretrisk cell`c' age `j'"
174.                 capture noisily estpost sum durretrisk if Emp_Lig==1 & ageend == `j' & cellret==`c'
175.                 if _rc!=2000{
176.                         putexcel set results/${samplefolder}/5_age, sheet("rho_cell`c'") modify
177.                         putexcel A`r'=("`j'") D1=("time at risk") D`r'=matrix(e(sum))
178.                         `putexcelclose'
179.                 }
180.  
.                 * Transitions : not right-censored (end==1) and pretrans==1 or 6
.                 di "transition cell`c' age `j'"
181.                 capture noisily estpost sum end if (pretrans==1 | pretrans==6) & ageend == `j' & cellret==`c'
182.                 if _rc!=2000{
183.                         putexcel set results/${samplefolder}/5_age, sheet("rho_cell`c'") modify
184.                         putexcel C1=("transitions") C`r'=matrix(e(sum))
185.                         `putexcelclose' 
186.                         if ${iab}==1{
187.                         putexcel set results/${samplefolder}/5_age, sheet("rho_cell`c'") modify
188.                         putexcel B1=("rho") B`r'=formula(=C`r'/D`r')
189.                         `putexcelclose'
190.                         }
191.                 }
192.         }
193. }
194. }
195. 
.         /* -------------------------------------------- */
.          *  (8b) Job loss rate delta 
.         /* -------------------------------------------- */      
.         
.         * 1) deltalig           -> Duration of lignite normal emp => that ends in unemployment, if unemployment ends in any employment later (pretrans 7 and 8)
.         * 2) deltanonlig        -> Duration of non-lignite normal emp => that ends in unemployment, if unemployment ends in any employment later (pretrans 11 and 12)
.         
.         * by cells
.                 di "Job loss rate (by cell - unemployment after lignite only)"
196.                 * Number of distinct persons in cells at risk (working in lignite)
.                 cap drop dummy infopers countinfopers
197.                 gen dummy=0
198.                 replace dummy=1 if Emp_Lig==1 & durrisk!=0
199.                 bys persnr cell: egen infopers=max(dummy)
200.                 bys persnr cell: gen countinfopers=1 if (infopers==1 & _n==1)
201.                 
.                 forvalues i = 1/$cellnumber {
202.                 local j=`i'+2
203.                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("deltalig") modify
204.                 putexcel A`j'=("`i'")
205.                 `putexcelclose'
206.                 * Number of distinct persons in cells at risk (working in lignite)
.                 di "distinct cell `i'"
207.                 capture noisily estpost sum countinfopers if cell == `i'
208.                 if _rc!=2000{
209.                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("deltalig") modify
210.                 putexcel D1=("nb distinct persons at risk") D`j'=matrix(e(count))
211.                 `putexcelclose'
212.                 }
213.                 * Exposure time : time of employment in lignite (Emp_Lig1==1)
.                 di "durrisk cell `i'"
214.                 capture noisily estpost sum durrisk if Emp_Lig==1 & cell == `i'
215.                 if _rc!=2000{
216.                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("deltalig") modify
217.                 putexcel E1=("time at risk") E`j'=matrix(e(sum))
218.                 `putexcelclose'
219.                 }
220.                 *deltalig
.                 * Transitions : not right-censored (end==1) and pretrans==7 or 8
.                 di "transition cell `i'"
221.                 capture noisily estpost sum end if (pretrans==7 | pretrans==8) & cell == `i'
222.                 if _rc!=2000{
223.                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("deltalig") modify
224.                 putexcel C1=("transitions") C`j'=matrix(e(sum))
225.                 `putexcelclose'
226.                         if ${iab}==1{
227.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("deltalig") modify
228.                                 putexcel B1=("deltalig") B`j'=formula(=C`j'/E`j')
229.                                 `putexcelclose'
230.                         }
231.                 }
232.         }
233.                         
.         di "Job loss rate (by cell -unemployment after nonlignite only)"
234.         * Number of distinct persons in cells at risk (working in non lignite)
.         cap drop dummy infopers countinfopers
235.         gen dummy=0
236.         replace dummy=1 if Emp_NonLig==1 & durrisk!=0
237.         bys persnr cell: egen infopers=max(dummy)
238.         bys persnr cell: gen countinfopers=1 if (infopers==1 & _n==1)
239.         
.         forvalues i = 1/$cellnumber {
240.                 local j=`i'+2
241.                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("deltanonlig") modify
242.                 putexcel A`j'=("`i'")
243.                 `putexcelclose'
244.                 * Number of distinct persons in cells at risk (working in non lignite)
.                 di "distinct cell `i'"
245.                 capture noisily estpost sum countinfopers if cell == `i'
246.                 if _rc!=2000{
247.                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("deltanonlig") modify
248.                 putexcel D1=("nb distinct persons at risk") D`j'=matrix(e(count))
249.                 `putexcelclose'
250.                 }
251.                 * Exposure time : time of employment NOT in lignite (Emp_NoLig==1) 
.                 di "durrisk cell `i'"
252.                 capture noisily estpost sum durrisk if Emp_NonLig==1 & cell == `i'
253.                 if _rc!=2000{
254.                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("deltanonlig") modify
255.                 putexcel E1=("time at risk") E`j'=matrix(e(sum))
256.                 `putexcelclose'
257.                 }
258.                 *deltanonlig
.                 * Transitions : not right-censored (end==1) and pretrans==11 or 12
.                 di "transition cell `i'"
259.                 capture noisily estpost sum end if (pretrans==11 | pretrans==12) & cell == `i'
260.                 if _rc!=2000{
261.                         putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("deltanonlig") modify
262.                         putexcel C1=("transitions") C`j'=matrix(e(sum))
263.                         `putexcelclose'
264.                         if ${iab}==1{
265.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("deltanonlig") modify
266.                                 putexcel B1=("deltanonlig") B`j'=formula(=C`j'/E`j')
267.                                 `putexcelclose'
268.                         }
269.                 }
270.         }
271. 
.                 
.         /* -------------------------------------------------- */
.          *  (8c) Job offer arrival rate / Job finding rate lambda
.         /* -------------------------------------------------- */        
. 
.                 * 1) lambdalig          -> finding a job in lignite if unemployed (=0 by assumption) after lignite - posttrans = 8 (lignite - unemp/ALMP/marg - job in lignite)
.                 * 2) lambdanonlig       -> finding a job in non-lignite if unemployed after lignite - posttrans = 7 (lignite - unemp/ALMP/marg - job in non-lignite)
.                 * 3) lambdazerolig      -> finding a job in non-lignite if unemployed after non-lignite - posttrans = 11 (non-lignite - unemp/ALMP/marg - job in non-lignite)   
.                 
.                 * we can define the estimator as the total number of transitions divided by the total exposure time(=time in unemployment after lignite or after non lignite)            
.         
.         * by cells
. 
.         di "Lignite leavers' Job finding rate (ending in a new lignite job)"
272.         * Number of distinct persons in cells unemployed after lignite
.                         * - ending in non lignite or lignite employment (end==1 & (posttrans==7|posttrans==8))  
.                         * - OR censored (end==0)
.         cap drop dummy infopers countinfopers
273.         gen dummy=0
274.         replace dummy=1 if Unemp_postLig==1 & ((end==1 & (posttrans==7 | posttrans==8)) | end==0) & durrisk!=0
275.         bys persnr cell: egen infopers=max(dummy)
276.         bys persnr cell: gen countinfopers=1 if (infopers==1 & _n==1)
277.                 
.         forvalues i = 1/$cellnumber {
278.                 local j=`i'+2
279.                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdalig") modify
280.                 putexcel A`j'=("`i'")
281.                 `putexcelclose'
282.                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdanonlig") modify
283.                 putexcel A`j'=("`i'")
284.                 `putexcelclose'
285.                 * Number of distinct persons in cells unemployed after lignite
.                                 * - ending in non lignite or lignite employment (end==1 & (posttrans==7|posttrans==8))  
.                                 * - OR censored (end==0)
.                 di "distinct cell `i'"
286.                 capture noisily estpost sum countinfopers if cell == `i'
287.                 if _rc!=2000{
288.                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdalig") modify
289.                 putexcel D1=("nb distinct persons at risk") D`j'=matrix(e(count))
290.                 `putexcelclose'
291.                 *same for lambdanonlig
.                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdanonlig") modify
292.                 putexcel D1=("nb distinct persons at risk") D`j'=matrix(e(count))
293.                 `putexcelclose'
294.                 }
295.                 * Exposure time : time of unemployment after lignite (Unemp_postLig==1) 
.                                 * - ending in non lignite or lignite employment (end==1 & (posttrans==7|posttrans==8))  
.                                 * - OR censored (end==0)
.                 di "durrisk cell `i'"
296.                 capture noisily estpost sum durrisk if Unemp_postLig==1 & ((end==1 & (posttrans==7 | posttrans==8)) | end==0) & cell == `i'
297.                 if _rc!=2000{
298.                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdalig") modify
299.                 putexcel E1=("time at risk") E`j'=matrix(e(sum))
300.                 *same for lambdanonlig
.                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdanonlig") modify
301.                 putexcel E1=("time at risk") E`j'=matrix(e(sum))
302.                 `putexcelclose'
303.                 }
304.                 *lambdalig
.                 * Transitions : not right-censored (end==1) and posttrans==8 (Lig)
.                 di "transition cell `i'"
305.                 capture noisily estpost sum end if posttrans==8 & cell == `i'
306.                 if _rc!=2000{
307.                         putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdalig") modify
308.                         putexcel C1=("transitions") C`j'=matrix(e(sum))
309.                         `putexcelclose'
310.                         if ${iab}==1{
311.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdalig") modify
312.                                 putexcel B1=("lambdalig") B`j'=formula(=C`j'/E`j')
313.                                 `putexcelclose'
314.                         }
315.                 }
316.         }
317.                         
.         di "Lignite leavers' Job finding rate (ending in a new non-lignite job)"
318.         
.         * Number of distinct persons in cells:  same as lambdalig, cf above
.         
.         forvalues i = 1/$cellnumber {
319.                 local j=`i'+2
320.                 * Exposure time : same as lambdalig, cf above
.                 *lambdalig
.                 * Transitions : not right-censored (end==1) and posttrans==7 (NonLig)
.                 di "transition cell `i'"
321.                 capture noisily estpost sum end if posttrans==7 & cell == `i'
322.                 if _rc!=2000{
323.                         putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdanonlig") modify
324.                         putexcel C1=("transitions") C`j'=matrix(e(count))
325.                         `putexcelclose'
326.                         if ${iab}==1{
327.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdanonlig") modify
328.                                 putexcel B1=("lambdanonlig") B`j'=formula(=C`j'/E`j')
329.                                 `putexcelclose'
330.                         }
331.                 }
332.         }
333. 
.         di "Non Lignite leavers' Job finding rate (ending in a new non-lignite job)"
334.         * Number of distinct persons in cells unemployed after non lignite
.                 * - ending in Non lignite or lignite employment (end==1 & (posttrans==11 | posttrans==12) 
.                 * - OR censored (end==0)
.         cap drop dummy infopers countinfopers
335.         gen dummy=0
336.         replace dummy=1 if Unemp_postNonLig==1 & ((end==1 & (posttrans==11 | posttrans==12)) | end==0) & durrisk!=0
337.         bys persnr cell: egen infopers=max(dummy)
338.         bys persnr cell: gen countinfopers=1 if (infopers==1 & _n==1)
339.         
.         forvalues i = 1/$cellnumber {
340.                 local j=`i'+2
341.                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdazerolig") modify
342.                 putexcel A`j'=("`i'")
343.                 `putexcelclose'
344.                 * Number of distinct persons in cells unemployed after non lignite
.                 * - ending in Non lignite or lignite employment (end==1 & (posttrans==11 | posttrans==12) 
.                 * - OR censored (end==0)
.                 di "distinct cell `i'"
345.                 capture noisily estpost sum countinfopers if cell == `i'
346.                 if _rc!=2000{
347.                         putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdazerolig") modify
348.                         putexcel D1=("nb distinct persons at risk") D`j'=matrix(e(count))
349.                         `putexcelclose'
350.                 }
351.                 * Exposure time : time of unemployment after non lignite (Unemp_postnonLig==1) 
.                                 * - ending in Non lignite or lignite employment (end==1 & (posttrans==11 | posttrans==12) 
.                                 * - OR censored (end==0)
.                 di "durrisk cell `i'"
352.                 capture noisily estpost sum durrisk if Unemp_postNonLig==1 & ((end==1 & (posttrans==11 | posttrans==12)) | end==0) & cell == `i'
353.                 if _rc!=2000{
354.                         putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdazerolig") modify
355.                         putexcel E1=("time at risk") E`j'=matrix(e(sum))
356.                         `putexcelclose'
357.                 }
358.                 *lambdazerolig
.                 * Transitions : not right-censored (end==1) and posttrans==11
.                 di "transition cell `i'"
359.                 capture noisily estpost sum end if posttrans==11 & cell == `i'
360.                 if _rc!=2000{
361.                         putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdazerolig") modify
362.                         putexcel C1=("transitions") C`j'=matrix(e(sum))
363.                         `putexcelclose'
364.                         if ${iab}==1{
365.                                 putexcel set results/${samplefolder}/5_sample${sample}_wholesampleAndcells_estimates_Censoring, sheet("lambdazerolig") modify
366.                                 putexcel B1=("lambdazerolig") B`j'=formula(=C`j'/E`j')
367.                                 `putexcelclose'
368.                         }
369.                 }
370.         }
371. 
. }               
Analysis on the whole sample

       keep |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |  1,382,388      100.00      100.00
------------+-----------------------------------
      Total |  1,382,388      100.00

                duration in labour mkt status
-------------------------------------------------------------
no observations

                      |    keep
             pretrans |         1 |     Total
----------------------+-----------+----------
Not pre (observed) tr | 1,072,523 | 1,072,523 
pre retirement (witho |    10,558 |    10,558 
pre trans'n 2 vocatio |       163 |       163 
pre trans'n 2 other n |    25,088 |    25,088 
pre trans'n to unem/A |    14,760 |    14,760 
pre trans'n 2 unemp/A |    22,349 |    22,349 
pre trans'n 2 unemp/A |     3,935 |     3,935 
pre trans'n 2 black h |    54,826 |    54,826 
pre transition out of |   173,673 |   173,673 
pre transition out of |     4,513 |     4,513 
----------------------+-----------+----------
                Total | 1,382,388 | 1,382,388 

                      |    keep
            posttrans |         1 |     Total
----------------------+-----------+----------
Not post trans'n out  | 1,079,772 | 1,079,772 
in retirement (no min |       349 |       349 
 post-lignite vocatio |       158 |       158 
post-lignite normal e |    32,968 |    32,968 
post-lignite unemp/AL |    14,711 |    14,711 
post-lignite unemp/AL |    22,273 |    22,273 
post-lignite unemp/AL |     3,875 |     3,875 
post-lignite black ho |    51,887 |    51,887 
transition out of NON |   171,901 |   171,901 
transition out of NON |     4,494 |     4,494 
----------------------+-----------+----------
                Total | 1,382,388 | 1,382,388 
(0 observations deleted)

                duration in labour mkt status
-------------------------------------------------------------
      Percentiles      Smallest
 1%            4              1
 5%           23              1
10%           36              1       Obs           1,382,388
25%           95              1       Sum of wgt.   1,382,388

50%          306                      Mean           766.1165
                        Largest       Std. dev.      1290.785
75%          790          14334
90%         2002          14346       Variance        1666127
95%         3404          14513       Skewness       3.470973
99%         6719          14640       Kurtosis       18.02178

                           durrisk
-------------------------------------------------------------
      Percentiles      Smallest
 1%            2              0
 5%           20              0
10%           34              0       Obs           1,382,388
25%           93              0       Sum of wgt.   1,382,388

50%          304                      Mean           759.6135
                        Largest       Std. dev.      1282.552
75%          777          14334
90%         1979          14346       Variance        1644940
95%         3379          14513       Skewness        3.46666
99%         6594          14640       Kurtosis       18.02431
Lignite leavers' Retirement probability (according to cell)
(188,628 real changes made)
(1,238,934 missing values generated)
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 1

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      5247       5247          1          0          0          1          1       5247 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durretrisk cell 1

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      6164       6164   900.9661   784064.7   885.4743          1       5075    5553555 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 1
no observations
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 2

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      1072       1072          1          0          0          1          1       1072 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durretrisk cell 2

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      1164       1164   905.1942   532597.6   729.7928          5       4808    1053646 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 2
no observations
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 3

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      5465       5465          1          0          0          1          1       5465 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durretrisk cell 3

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      7764       7764   2811.539    8012601   2830.654          1      11963   2.18e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 3

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        24         24   .5416667    .259058   .5089774          0          1         13 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 4

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      1197       1197          1          0          0          1          1       1197 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durretrisk cell 4

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      1812       1812   1180.994    1512133   1229.688          1       8544    2139962 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 4

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA          4          1          0          0          1          1          4 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 5

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      7519       7519          1          0          0          1          1       7519 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durretrisk cell 5

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |     10065      10065   2769.632    6492521   2548.043          0      12053   2.79e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 5

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      3964       3964   .9846115   .0151555   .1231077          0          1       3903 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 6

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      2307       2307          1          0          0          1          1       2307 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durretrisk cell 6

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      3237       3237    2117.09    2784169   1668.583          0       9088    6853020 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 6

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       510        510          1          0          0          1          1        510 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 7

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |     13855      13855          1          0          0          1          1      13855 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durretrisk cell 7

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |     16989      16989    741.292   551586.4   742.6886          1       4897   1.26e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 7
no observations
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 8

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      2351       2351          1          0          0          1          1       2351 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durretrisk cell 8

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      2767       2767   753.3553     442135   664.9324          1       4077    2084534 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 8
no observations
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 9

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |     30856      30856          1          0          0          1          1      30856 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durretrisk cell 9

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |     42802      42802   1959.145    5071185   2251.929          1      11688   8.39e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 9

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       134        134   .7238806   .2013803   .4487542          0          1         97 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 10

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      4357       4357          1          0          0          1          1       4357 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durretrisk cell 10

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      5727       5727   1461.437    2060246   1435.356          1      10012    8369648 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 10

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |         5          5          1          0          0          1          1          5 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 11

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |     31215      31215          1          0          0          1          1      31215 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durretrisk cell 11

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |     40462      40462   2553.297    6611406   2571.265          0      14513   1.03e+08 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 11

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |     14171      14171   .9451697   .0518276   .2276567          0          1      13394 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 12

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      9073       9073          1          0          0          1          1       9073 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durretrisk cell 12

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |     12387      12387   1646.107    2273411   1507.783          0      13408   2.04e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 12

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      1533       1533   .9993477   .0006523   .0255405          0          1       1532 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 13

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       829        829          1          0          0          1          1        829 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durretrisk cell 13

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       941        941   762.6525   689999.5   830.6621          1       5290     717656 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 13
no observations
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 14

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       111        111          1          0          0          1          1        111 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durretrisk cell 14

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       122        122   794.5984   401002.9   633.2479         25       3378      96941 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 14
no observations
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 15

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      1073       1073          1          0          0          1          1       1073 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durretrisk cell 15

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      1359       1359    1075.24    2560699   1600.218          1      11323    1461251 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 15

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         42   .9761905   .0238095   .1543033          0          1         41 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 16

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |        NA         79          1          0          0          1          1         79 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durretrisk cell 16

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       138        138   734.3406    1069719   1034.272          1       6243     101339 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 16
no observations
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 17

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      1245       1245          1          0          0          1          1       1245 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durretrisk cell 17

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      1585       1585   1735.249    3984303   1996.072          0      11688    2750370 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 17

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       630        630   .9952381   .0047468   .0688968          0          1        627 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 18

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |        NA         91          1          0          0          1          1         91 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durretrisk cell 18

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       158        158   1157.323    1944200   1394.346          0       6209     182857 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 18

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         39          1          0          0          1          1         39 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 19

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      4622       4622          1          0          0          1          1       4622 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durretrisk cell 19

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      5594       5594    606.458   442060.7   664.8765          1       4662    3392526 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 19
no observations
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 20

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       386        386          1          0          0          1          1        386 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durretrisk cell 20

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       465        465   621.7484   314172.6    560.511          2       3621     289113 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 20
no observations
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 21

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      9249       9249          1          0          0          1          1       9249 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durretrisk cell 21

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |     12028      12028   1065.085    1717197   1310.419          1      11323   1.28e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 21

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       140        140   .9642857   .0346865   .1862432          0          1        135 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 22

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       651        651          1          0          0          1          1        651 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durretrisk cell 22

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       831        831   1080.789    1166682   1080.131          5       6575     898136 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 22
no observations
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 23

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      5803       5803          1          0          0          1          1       5803 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durretrisk cell 23

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      7348       7348   1809.318    4046463   2011.582          0      12053   1.33e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 23

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      2519       2519   .9495832    .047894    .218847          0          1       2392 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 24

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       882        882          1          0          0          1          1        882 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durretrisk cell 24

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      1271       1271   993.3493    1557053   1247.819          0       9101    1262547 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 24

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       172        172          1          0          0          1          1        172 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
(12,863 real changes made)
(1,369,525 missing values generated)

file results/two/5_age.xlsx saved

durretrisk age 50

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      5013       5013   2284.683    7230000   2688.866          1      12053   1.15e+07 
file results/two/5_age.xlsx saved
transition age 

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       187        187   .8449198   .1317348   .3629529          0          1        158 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk age 51

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      6284       6284   2624.216    8657657    2942.39          1      14513   1.65e+07 
file results/two/5_age.xlsx saved
transition age 

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       476        476   .9369748   .0591774    .243264          0          1        446 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk age 52

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      6057       6057   2219.068    6882963    2623.54          1      12053   1.34e+07 
file results/two/5_age.xlsx saved
transition age 

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       536        536   .8097015    .154373   .3929033          0          1        434 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk age 53

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      6321       6321   2041.768    5889558   2426.841          1      12053   1.29e+07 
file results/two/5_age.xlsx saved
transition age 

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       581        581   .8347676   .1381684   .3717102          0          1        485 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk age 54

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      7554       7554   2219.044    6159006   2481.734          1      12053   1.68e+07 
file results/two/5_age.xlsx saved
transition age 

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      1480       1480    .977027   .0224604   .1498679          0          1       1446 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk age 55

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |     12875      12875   2153.612    4939983   2222.607          0      12053   2.77e+07 
file results/two/5_age.xlsx saved
transition age 

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      5499       5499   .9941808   .0057864   .0760686          0          1       5467 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk age 56

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      8345       8345   2148.033    4070038   2017.433          0      12053   1.79e+07 
file results/two/5_age.xlsx saved
transition age 

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      3410       3410   .9956012   .0043808   .0661873          0          1       3395 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk age 57

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      6241       6241   2071.969    3974268   1993.557          0      12053   1.29e+07 
file results/two/5_age.xlsx saved
transition age 

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      2545       2545   .9933202   .0066378   .0814724          0          1       2528 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk age 58

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      5257       5257   2715.285    5414322    2326.87          0      12047   1.43e+07 
file results/two/5_age.xlsx saved
transition age 

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      1787       1787   .9837717   .0159739   .1263879          0          1       1758 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk age 59

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      4559       4559   2454.552    4398811   2097.334          0      12053   1.12e+07 
file results/two/5_age.xlsx saved
transition age 

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      1791       1791   .9899497   .0099548   .0997738          0          1       1773 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk age 60

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      4786       4786    2589.22    3830118   1957.069          0      13408   1.24e+07 
file results/two/5_age.xlsx saved
transition age 

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      2693       2693    .928704   .0662374   .2573663          0          1       2501 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk age 61

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      2241       2241   2368.183    5124750   2263.791          0      10542    5307098 
file results/two/5_age.xlsx saved
transition age 

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       908        908   .7103524   .2059787   .4538488          0          1        645 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk age 62

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      2053       2053   1953.583    4627811   2151.235          0       9175    4010706 
file results/two/5_age.xlsx saved
transition age 

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       962        962   .8108108   .1535563   .3918626          0          1        780 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk age 63

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      2133       2133    2022.12    4517743   2125.498          0       9496    4313183 
file results/two/5_age.xlsx saved
transition age 

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      1549       1549   .9857973     .01401    .118364          0          1       1527 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk age 64

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       421        421   2378.233    5359687     2315.1          0      11017    1001236 
file results/two/5_age.xlsx saved
transition age 

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       263        263     .78327   .1704061   .4128027          0          1        206 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk age 65

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       349        349   2232.072    5832721   2415.103          0      10897     778993 
file results/two/5_age.xlsx saved
transition age 

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       276        276          1          0          0          1          1        276 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
(25,026 missing values generated)

          group(frau educ2 macro) |      Freq.     Percent        Cum.
----------------------------------+-----------------------------------
mann keine abg. Ausbild. hi-unemp |     81,192        5.87        5.87
mann keine abg. Ausbild. lo-unemp |     62,500        4.52       10.39
    mann abg. Ausbildung hi-unemp |    396,953       28.72       39.11
    mann abg. Ausbildung lo-unemp |    522,269       37.78       76.89
frau keine abg. Ausbild. hi-unemp |     15,678        1.13       78.02
frau keine abg. Ausbild. lo-unemp |     13,042        0.94       78.97
    frau abg. Ausbildung hi-unemp |    116,038        8.39       87.36
    frau abg. Ausbildung lo-unemp |    149,690       10.83       98.19
                                . |     25,026        1.81      100.00
----------------------------------+-----------------------------------
                            Total |  1,382,388      100.00
(1,382,388 missing values generated)
(81,192 real changes made)
(62,500 real changes made)
(396,953 real changes made)
(522,269 real changes made)
(15,678 real changes made)
(13,042 real changes made)
(116,038 real changes made)
(149,690 real changes made)

    cellret |      Freq.     Percent        Cum.
------------+-----------------------------------
          5 |     81,192        5.87        5.87
          6 |     62,500        4.52       10.39
         11 |    396,953       28.72       39.11
         12 |    522,269       37.78       76.89
         17 |     15,678        1.13       78.02
         18 |     13,042        0.94       78.97
         23 |    116,038        8.39       87.36
         24 |    149,690       10.83       98.19
          . |     25,026        1.81      100.00
------------+-----------------------------------
      Total |  1,382,388      100.00
durretrisk cell5 age 50
durretrisk cell5 age 50

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       536        536   2572.649    8671831   2944.797          2      11688    1378940 
file results/two/5_age.xlsx saved
transition cell5 age 50

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA   .6190476    .247619   .4976134          0          1         13 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell5 age 51
durretrisk cell5 age 51

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       748        748   3134.972   1.06e+07   3261.875          1      12053    2344959 
file results/two/5_age.xlsx saved
transition cell5 age 51

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA   .9534884     .04487   .2118255          0          1         82 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell5 age 52
durretrisk cell5 age 52

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       613        613   2432.179    7886768   2808.339          3      11688    1490926 
file results/two/5_age.xlsx saved
transition cell5 age 52

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA        .84   .1362162   .3690748          0          1         63 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell5 age 53
durretrisk cell5 age 53

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       655        655   2235.588    6800702   2607.816          1      11688    1464310 
file results/two/5_age.xlsx saved
transition cell5 age 53

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA   .8658537   .1175851   .3429068          0          1         71 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell5 age 54
durretrisk cell5 age 54

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       881        881    2787.91    8028802   2833.514          1      12053    2456149 
file results/two/5_age.xlsx saved
transition cell5 age 54

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       230        230   .9913043   .0086577   .0930467          0          1        228 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell5 age 55
durretrisk cell5 age 55

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      1146       1146   2655.387    6885556   2624.034          1      10240    3043073 
file results/two/5_age.xlsx saved
transition cell5 age 55

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       469        469   .9957356   .0042553   .0652325          0          1        467 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell5 age 56
durretrisk cell5 age 56

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       918        918    2696.07    6525524    2554.51          1      12053    2474992 
file results/two/5_age.xlsx saved
transition cell5 age 56

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       377        377          1          0          0          1          1        377 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell5 age 57
durretrisk cell5 age 57

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       728        728   2619.514    5260993   2293.685          1       9497    1907006 
file results/two/5_age.xlsx saved
transition cell5 age 57

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       267        267   .9962547   .0037453    .061199          0          1        266 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell5 age 58
durretrisk cell5 age 58

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      1151       1151   3301.094    5875853   2424.016          0      11688    3799559 
file results/two/5_age.xlsx saved
transition cell5 age 58

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       544        544   .9963235   .0036697    .060578          0          1        542 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell5 age 59
durretrisk cell5 age 59

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      1011       1011   2742.958    3992850   1998.212          1      11688    2773131 
file results/two/5_age.xlsx saved
transition cell5 age 59

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       628        628   .9984076   .0015924   .0399043          0          1        627 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell5 age 60
durretrisk cell5 age 60

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       891        891   2919.678    3789557   1946.678          1       9678    2601433 
file results/two/5_age.xlsx saved
transition cell5 age 60

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       653        653   .9892802   .0106211   .1030588          0          1        646 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell5 age 61
durretrisk cell5 age 61

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       315        315   2790.292    4684028   2164.261          1       9528     878942 
file results/two/5_age.xlsx saved
transition cell5 age 61

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       179        179   .9608939    .037788   .1943913          0          1        172 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell5 age 62
durretrisk cell5 age 62

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       153        153   2429.516    4619440   2149.288          2       7486     371716 
file results/two/5_age.xlsx saved
transition cell5 age 62

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA   .9534884     .04487   .2118255          0          1         82 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell5 age 63
durretrisk cell5 age 63

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       234        234   2733.987    3888161   1971.842         11       9313     639753 
file results/two/5_age.xlsx saved
transition cell5 age 63

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       200        200          1          0          0          1          1        200 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell5 age 64
durretrisk cell5 age 64

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   3392.474    9370945     3061.2         25       9712     128914 
file results/two/5_age.xlsx saved
transition cell5 age 64

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         27 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell5 age 65
durretrisk cell5 age 65

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   2607.319    7366255   2714.085        243      10897     122544 
file results/two/5_age.xlsx saved
transition cell5 age 65

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         40 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell6 age 50
durretrisk cell6 age 50

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       220        220   1659.527    2343830   1530.957         12       6209     365096 
file results/two/5_age.xlsx saved
transition cell6 age 50

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         11 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell6 age 51
durretrisk cell6 age 51

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       258        258   1984.221    2397680   1548.445          2       6209     511929 
file results/two/5_age.xlsx saved
transition cell6 age 51

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         17 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell6 age 52
durretrisk cell6 age 52

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       251        251   2169.251    2635644   1623.467          1       6263     544482 
file results/two/5_age.xlsx saved
transition cell6 age 52

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell6 age 53
durretrisk cell6 age 53

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       285        285   2144.242    2741814   1655.842          2       9088     611109 
file results/two/5_age.xlsx saved
transition cell6 age 53

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell6 age 54
durretrisk cell6 age 54

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       284        284    2246.19    2752647    1659.11          2       6940     637918 
file results/two/5_age.xlsx saved
transition cell6 age 54

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell6 age 55
durretrisk cell6 age 55

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       397        397   2521.806    3448517   1857.018          7       8279    1001157 
file results/two/5_age.xlsx saved
transition cell6 age 55

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       121        121          1          0          0          1          1        121 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell6 age 56
durretrisk cell6 age 56

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       326        326   2371.049    2696258   1642.029         23       6359     772962 
file results/two/5_age.xlsx saved
transition cell6 age 56

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        34         34          1          0          0          1          1         34 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell6 age 57
durretrisk cell6 age 57

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       279        279   2111.391    2830786   1682.494          6       6209     589078 
file results/two/5_age.xlsx saved
transition cell6 age 57

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell6 age 58
durretrisk cell6 age 58

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       242        242   2072.388    2946375   1716.501          0       5934     501518 
file results/two/5_age.xlsx saved
transition cell6 age 58

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell6 age 59
durretrisk cell6 age 59

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       216        216   2178.963    2633819   1622.905          9       5569     470656 
file results/two/5_age.xlsx saved
transition cell6 age 59

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell6 age 60
durretrisk cell6 age 60

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       159        159   1899.057    2455310   1566.943          1       6209     301950 
file results/two/5_age.xlsx saved
transition cell6 age 60

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell6 age 61
durretrisk cell6 age 61

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   1897.194    2354738   1534.515          0       4475     176439 
file results/two/5_age.xlsx saved
transition cell6 age 61

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell6 age 62
durretrisk cell6 age 62

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       101        101   1565.327    2230268   1493.408          0       4564     158098 
file results/two/5_age.xlsx saved
transition cell6 age 62

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell6 age 63
durretrisk cell6 age 63

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   1645.939    2430153   1558.895          0       5266     161302 
file results/two/5_age.xlsx saved
transition cell6 age 63

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell6 age 64
durretrisk cell6 age 64

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   2090.167    4608549   2146.753          4       5934      25082 
file results/two/5_age.xlsx saved
transition cell6 age 64

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell6 age 65
durretrisk cell6 age 65

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   1745.538    2211193   1487.008         91       4322      22692 
file results/two/5_age.xlsx saved
transition cell6 age 65

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell11 age 50
durretrisk cell11 age 50

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      2642       2642   2658.772    9097489   3016.204          1      11688    7024475 
file results/two/5_age.xlsx saved
transition cell11 age 50

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA   .7710843   .1786659   .4226889          0          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell11 age 51
durretrisk cell11 age 51

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      3419       3419   3054.081   1.05e+07   3243.721          1      14513   1.04e+07 
file results/two/5_age.xlsx saved
transition cell11 age 51

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       263        263   .9125475   .0801091   .2830356          0          1        240 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell11 age 52
durretrisk cell11 age 52

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      3210       3210   2575.458    8799410    2966.38          1      12053    8267219 
file results/two/5_age.xlsx saved
transition cell11 age 52

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       318        318   .7389937   .1934905   .4398755          0          1        235 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell11 age 53
durretrisk cell11 age 53

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      3213       3213   2267.497    7342416   2709.689          1      12053    7285468 
file results/two/5_age.xlsx saved
transition cell11 age 53

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       292        292   .7363014   .1948289   .4413943          0          1        215 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell11 age 54
durretrisk cell11 age 54

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      4078       4078   2392.757    7234419   2689.688          1      12053    9757663 
file results/two/5_age.xlsx saved
transition cell11 age 54

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       919        919   .9651795   .0336446   .1834247          0          1        887 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell11 age 55
durretrisk cell11 age 55

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      7210       7210   2254.517    5436974   2331.732          1      12053   1.63e+07 
file results/two/5_age.xlsx saved
transition cell11 age 55

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      3313       3313   .9912466   .0086794   .0931633          0          1       3284 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell11 age 56
durretrisk cell11 age 56

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      4573       4573    2205.23    4356972   2087.336          1      12053   1.01e+07 
file results/two/5_age.xlsx saved
transition cell11 age 56

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      2191       2191   .9940666   .0059009    .076817          0          1       2178 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell11 age 57
durretrisk cell11 age 57

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      3170       3170   2179.964    4490714    2119.13          0      12053    6910487 
file results/two/5_age.xlsx saved
transition cell11 age 57

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      1608       1608   .9900498   .0098574   .0992843          0          1       1592 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell11 age 58
durretrisk cell11 age 58

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      2355       2355   3044.994    6033892   2456.398          0      10926    7170962 
file results/two/5_age.xlsx saved
transition cell11 age 58

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      1032       1032   .9825581   .0171543   .1309743          0          1       1014 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell11 age 59
durretrisk cell11 age 59

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      1916       1916   2873.682    5219556   2284.635          0      12053    5505974 
file results/two/5_age.xlsx saved
transition cell11 age 59

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       977        977   .9877175   .0121441   .1102001          0          1        965 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell11 age 60
durretrisk cell11 age 60

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      2099       2099   2990.199    4035817   2008.934          0       9436    6276427 
file results/two/5_age.xlsx saved
transition cell11 age 60

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      1392       1392   .9310345   .0642554   .2534866          0          1       1296 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell11 age 61
durretrisk cell11 age 61

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       841        841    3198.57    6189378   2487.846          0      10542    2689997 
file results/two/5_age.xlsx saved
transition cell11 age 61

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       455        455   .6769231   .2191799   .4681666          0          1        308 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell11 age 62
durretrisk cell11 age 62

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       654        654   3164.113    5695296   2386.482          0       9175    2069330 
file results/two/5_age.xlsx saved
transition cell11 age 62

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       448        448   .6897321   .2144805   .4631204          0          1        309 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell11 age 63
durretrisk cell11 age 63

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       746        746    3387.28    5314821    2305.39          0       9496    2526911 
file results/two/5_age.xlsx saved
transition cell11 age 63

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       622        622   .9662379   .0326747   .1807615          0          1        601 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell11 age 64
durretrisk cell11 age 64

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       192        192   3101.854    5420112   2328.113          0      11017     595556 
file results/two/5_age.xlsx saved
transition cell11 age 64

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       141        141   .6312057   .2344478   .4841981          0          1         89 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell11 age 65
durretrisk cell11 age 65

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       136        136   3195.882    6874565   2621.939         10       9405     434640 
file results/two/5_age.xlsx saved
transition cell11 age 65

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       111        111          1          0          0          1          1        111 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell12 age 50
durretrisk cell12 age 50

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       584        584   1781.545    2308025   1519.218          4       7681    1040422 
file results/two/5_age.xlsx saved
transition cell12 age 50

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell12 age 51
durretrisk cell12 age 51

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       732        732   1761.566    2278494   1509.468         17      10310    1289466 
file results/two/5_age.xlsx saved
transition cell12 age 51

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell12 age 52
durretrisk cell12 age 52

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       765        765   1708.295    2041555    1428.83         11       6653    1306846 
file results/two/5_age.xlsx saved
transition cell12 age 52

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell12 age 53
durretrisk cell12 age 53

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       865        865   1727.179    2294990   1514.923          2       7310    1494010 
file results/two/5_age.xlsx saved
transition cell12 age 53

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell12 age 54
durretrisk cell12 age 54

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       886        886   1642.698    1946333   1395.111          6       8735    1455430 
file results/two/5_age.xlsx saved
transition cell12 age 54

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell12 age 55
durretrisk cell12 age 55

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      1256       1256   1944.726    2827034   1681.379          2       8582    2442576 
file results/two/5_age.xlsx saved
transition cell12 age 55

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       153        153          1          0          0          1          1        153 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell12 age 56
durretrisk cell12 age 56

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      1102       1102    1836.88    2143389   1464.032          0       6756    2024242 
file results/two/5_age.xlsx saved
transition cell12 age 56

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell12 age 57
durretrisk cell12 age 57

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      1038       1038   1706.342    2129371   1459.236          0       6209    1771183 
file results/two/5_age.xlsx saved
transition cell12 age 57

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell12 age 58
durretrisk cell12 age 58

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       987        987   1742.775    2217731   1489.205          3       8460    1720119 
file results/two/5_age.xlsx saved
transition cell12 age 58

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA   .9863014   .0136986   .1170411          0          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell12 age 59
durretrisk cell12 age 59

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       967        967   1706.984    2327774   1525.704          0       6300    1650654 
file results/two/5_age.xlsx saved
transition cell12 age 59

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell12 age 60
durretrisk cell12 age 60

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       850        850   1595.536    2240065   1496.685          0      13408    1356206 
file results/two/5_age.xlsx saved
transition cell12 age 60

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell12 age 61
durretrisk cell12 age 61

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       600        600   1372.348    2208743   1486.184          0       6117     823409 
file results/two/5_age.xlsx saved
transition cell12 age 61

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell12 age 62
durretrisk cell12 age 62

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       708        708   1136.133    1902110    1379.17          0       6574     804382 
file results/two/5_age.xlsx saved
transition cell12 age 62

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       177        177          1          0          0          1          1        177 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell12 age 63
durretrisk cell12 age 63

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       791        791    1060.02    1618823    1272.33          0       6483     838476 
file results/two/5_age.xlsx saved
transition cell12 age 63

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       489        489          1          0          0          1          1        489 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell12 age 64
durretrisk cell12 age 64

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       121        121   1425.702    2184682   1478.067          0       5387     172510 
file results/two/5_age.xlsx saved
transition cell12 age 64

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell12 age 65
durretrisk cell12 age 65

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       119        119   1265.092    2955628   1719.194          0       8766     150546 
file results/two/5_age.xlsx saved
transition cell12 age 65

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell17 age 50
durretrisk cell17 age 50

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       124        124   1106.919    2433813   1560.068          7       7777     137258 
file results/two/5_age.xlsx saved
transition cell17 age 50

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell17 age 51
durretrisk cell17 age 51

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       137        137   1252.445    4166732   2041.257          1      11688     171585 
file results/two/5_age.xlsx saved
transition cell17 age 51

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell17 age 52
durretrisk cell17 age 52

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       152        152   1340.441    4644076   2155.012          1      10774     203747 
file results/two/5_age.xlsx saved
transition cell17 age 52

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell17 age 53
durretrisk cell17 age 53

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       173        173   1177.942    3116699   1765.418         13      10774     203784 
file results/two/5_age.xlsx saved
transition cell17 age 53

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA   .9655172   .0344828   .1856953          0          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell17 age 54
durretrisk cell17 age 54

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       178        178   1589.725    4783362    2187.09         14       9131     282971 
file results/two/5_age.xlsx saved
transition cell17 age 54

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        55         55          1          0          0          1          1         55 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell17 age 55
durretrisk cell17 age 55

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       249        249   1624.743    3632765   1905.981         11      11139     404561 
file results/two/5_age.xlsx saved
transition cell17 age 55

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       120        120          1          0          0          1          1        120 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell17 age 56
durretrisk cell17 age 56

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       190        190   2009.042    3577752   1891.495         20       9131     381718 
file results/two/5_age.xlsx saved
transition cell17 age 56

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       116        116          1          0          0          1          1        116 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell17 age 57
durretrisk cell17 age 57

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   2106.784    3686549   1920.039          3       7456     204358 
file results/two/5_age.xlsx saved
transition cell17 age 57

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell17 age 58
durretrisk cell17 age 58

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA    2583.47    5272528   2296.199         10       9893     214428 
file results/two/5_age.xlsx saved
transition cell17 age 58

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell17 age 59
durretrisk cell17 age 59

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   2407.783    4003582   2000.895          0       6756     110758 
file results/two/5_age.xlsx saved
transition cell17 age 59

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA   .9565217   .0434783   .2085144          0          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell17 age 60
durretrisk cell17 age 60

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       124        124   2869.645    2395494   1547.738         46       6269     355836 
file results/two/5_age.xlsx saved
transition cell17 age 60

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       106        106          1          0          0          1          1        106 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell17 age 61
durretrisk cell17 age 61

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   2482.059    2324545   1524.646         59       5022      42195 
file results/two/5_age.xlsx saved
transition cell17 age 61

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA      .9375      .0625        .25          0          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell17 age 62
durretrisk cell17 age 62

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA    2624.75    2185152   1478.226       1186       4564      10499 
file results/two/5_age.xlsx saved
transition cell17 age 62

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell17 age 63
durretrisk cell17 age 63

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA     2520.8    1643016   1281.802       1277       3926      12604 
file results/two/5_age.xlsx saved
transition cell17 age 63

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell17 age 64
durretrisk cell17 age 64

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA    1302.75    2777850   1666.688         37       3713       5211 
file results/two/5_age.xlsx saved
transition cell17 age 64

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          .          .          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell17 age 65
durretrisk cell17 age 65

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA     4428.5   444624.5   666.8017       3957       4900       8857 
file results/two/5_age.xlsx saved
transition cell17 age 65

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          .          .          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell18 age 50
durretrisk cell18 age 50

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   1122.125    2246698   1498.899         64       4357       8977 
file results/two/5_age.xlsx saved
transition cell18 age 50

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell18 age 51
durretrisk cell18 age 51

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   901.9091    1573573   1254.421          1       4094       9921 
file results/two/5_age.xlsx saved
transition cell18 age 51

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell18 age 52
durretrisk cell18 age 52

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   973.2727    1445435   1202.263          1       4018      10706 
file results/two/5_age.xlsx saved
transition cell18 age 52
no observations
durretrisk cell18 age 53
durretrisk cell18 age 53

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   1008.667    2149926   1466.263          1       5545      15130 
file results/two/5_age.xlsx saved
transition cell18 age 53

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          .          .          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell18 age 54
durretrisk cell18 age 54

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   775.2222   734211.7   856.8615          1       3319      13954 
file results/two/5_age.xlsx saved
transition cell18 age 54

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell18 age 55
durretrisk cell18 age 55

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   1061.167    1048481   1023.953          1       3324      12734 
file results/two/5_age.xlsx saved
transition cell18 age 55

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell18 age 56
durretrisk cell18 age 56

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA      621.9     261883    511.745         51       1461       6219 
file results/two/5_age.xlsx saved
transition cell18 age 56

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell18 age 57
durretrisk cell18 age 57

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   1437.882    2360785   1536.485         30       5569      24444 
file results/two/5_age.xlsx saved
transition cell18 age 57

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell18 age 58
durretrisk cell18 age 58

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA     1531.2    2063793   1436.591          1       4112      22968 
file results/two/5_age.xlsx saved
transition cell18 age 58

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell18 age 59
durretrisk cell18 age 59

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   1479.333    2888000   1699.412         34       5295      22190 
file results/two/5_age.xlsx saved
transition cell18 age 59

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell18 age 60
durretrisk cell18 age 60

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA     2213.9    3735387   1932.715         19       6209      22139 
file results/two/5_age.xlsx saved
transition cell18 age 60

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell18 age 61
durretrisk cell18 age 61

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA        360   613104.4   783.0098          0       1949       2160 
file results/two/5_age.xlsx saved
transition cell18 age 61

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell18 age 62
durretrisk cell18 age 62

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   456.5714     725613   851.8292          0       2192       3196 
file results/two/5_age.xlsx saved
transition cell18 age 62

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell18 age 63
durretrisk cell18 age 63
no observations
transition cell18 age 63
no observations
durretrisk cell18 age 64
durretrisk cell18 age 64

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA     1104.5    1679945   1296.127        188       2021       2209 
file results/two/5_age.xlsx saved
transition cell18 age 64

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell18 age 65
durretrisk cell18 age 65

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA       5910          .          .       5910       5910       5910 
file results/two/5_age.xlsx saved
transition cell18 age 65

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          .          .          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell23 age 50
durretrisk cell23 age 50

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       650        650    1383.12    3484283   1866.624          1      11688     899028 
file results/two/5_age.xlsx saved
transition cell23 age 50

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA    .972973    .027027    .164399          0          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell23 age 51
durretrisk cell23 age 51

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       691        691   1507.185    4409574   2099.899          8      11688    1041465 
file results/two/5_age.xlsx saved
transition cell23 age 51

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA   .9661017   .0333139   .1825208          0          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell23 age 52
durretrisk cell23 age 52

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       772        772   1373.523    3858192   1964.228          1      11788    1060360 
file results/two/5_age.xlsx saved
transition cell23 age 52

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA      .9125   .0808544   .2843491          0          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell23 age 53
durretrisk cell23 age 53

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       764        764   1487.353    4324532   2079.551          3      12053    1136338 
file results/two/5_age.xlsx saved
transition cell23 age 53

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       114        114   .9385965   .0581431   .2411289          0          1        107 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell23 age 54
durretrisk cell23 age 54

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       853        853   1670.253    3996072   1999.018          1      12053    1424726 
file results/two/5_age.xlsx saved
transition cell23 age 54

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       194        194          1          0          0          1          1        194 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell23 age 55
durretrisk cell23 age 55

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |      1646       1646       2036    3613151   1900.829          1      11323    3351256 
file results/two/5_age.xlsx saved
transition cell23 age 55

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       875        875   .9988571   .0011429   .0338062          0          1        874 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell23 age 56
durretrisk cell23 age 56

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       892        892   1786.325    2161867   1470.329          5      12053    1593402 
file results/two/5_age.xlsx saved
transition cell23 age 56

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       557        557   .9964093   .0035842   .0598682          0          1        555 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell23 age 57
durretrisk cell23 age 57

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       408        408   1741.706    3031538   1741.131         31      10561     710616 
file results/two/5_age.xlsx saved
transition cell23 age 57

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       251        251          1          0          0          1          1        251 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell23 age 58
durretrisk cell23 age 58

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       150        150   2847.273    6684596   2585.458          7       9740     427091 
file results/two/5_age.xlsx saved
transition cell23 age 58

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA   .9512195    .047561   .2180848          0          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell23 age 59
durretrisk cell23 age 59

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       122        122   2813.434    7887964   2808.552          0      10013     343239 
file results/two/5_age.xlsx saved
transition cell23 age 59

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA       .875   .1141304    .337832          0          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell23 age 60
durretrisk cell23 age 60

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       213        213   3000.948    4813857    2194.05          0      10286     639202 
file results/two/5_age.xlsx saved
transition cell23 age 60

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       139        139   .7913669   .1663017   .4078011          0          1        110 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell23 age 61
durretrisk cell23 age 61

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   3751.723    5475205   2339.916         47       8766     352662 
file results/two/5_age.xlsx saved
transition cell23 age 61

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA   .2941176   .2107112   .4590328          0          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell23 age 62
durretrisk cell23 age 62

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   3339.672    3432709   1852.757         59       8766     193701 
file results/two/5_age.xlsx saved
transition cell23 age 62

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA   .6415094   .2343977   .4841463          0          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell23 age 63
durretrisk cell23 age 63

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA    2806.19    8092605    2844.75          0       8552      58930 
file results/two/5_age.xlsx saved
transition cell23 age 63

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA   .9333333   .0666667   .2581989          0          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell23 age 64
durretrisk cell23 age 64

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA     4978.1    6625767   2574.057        382       8766      49781 
file results/two/5_age.xlsx saved
transition cell23 age 64

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA   .4444444   .2777778   .5270463          0          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell23 age 65
durretrisk cell23 age 65

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA       5485    6272882   2504.572       3714       7256      10970 
file results/two/5_age.xlsx saved
transition cell23 age 65

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell24 age 50
durretrisk cell24 age 50

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   1446.768    2151287   1466.727         90       6209      99827 
file results/two/5_age.xlsx saved
transition cell24 age 50
no observations
durretrisk cell24 age 51
durretrisk cell24 age 51

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   1385.831    1533189    1238.22         91       5479     115024 
file results/two/5_age.xlsx saved
transition cell24 age 51

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          .          .          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell24 age 52
durretrisk cell24 age 52

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   1181.357    1594019   1262.545         14       5479      82695 
file results/two/5_age.xlsx saved
transition cell24 age 52
no observations
durretrisk cell24 age 53
durretrisk cell24 age 53

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   1451.976    2403513   1550.327         91       7944     120514 
file results/two/5_age.xlsx saved
transition cell24 age 53
no observations
durretrisk cell24 age 54
durretrisk cell24 age 54

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   1189.797    1636233   1279.153         12       6818      93994 
file results/two/5_age.xlsx saved
transition cell24 age 54

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell24 age 55
durretrisk cell24 age 55

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       123        123   1358.252    2663774   1632.107          3       9101     167065 
file results/two/5_age.xlsx saved
transition cell24 age 55

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell24 age 56
durretrisk cell24 age 56

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA    1167.17    1459591   1208.135         12       6117     109714 
file results/two/5_age.xlsx saved
transition cell24 age 56

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell24 age 57
durretrisk cell24 age 57

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   1026.277    1263416   1124.018         31       4993      85181 
file results/two/5_age.xlsx saved
transition cell24 age 57

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell24 age 58
durretrisk cell24 age 58

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   1155.903    1038185   1018.914         91       5479      83225 
file results/two/5_age.xlsx saved
transition cell24 age 58

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell24 age 59
durretrisk cell24 age 59

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   916.8065    1285721   1133.896          0       6940      85263 
file results/two/5_age.xlsx saved
transition cell24 age 59

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell24 age 60
durretrisk cell24 age 60

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   878.2135    1023164   1011.516          0       4018      78161 
file results/two/5_age.xlsx saved
transition cell24 age 60

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA          NA         1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell24 age 61
durretrisk cell24 age 61

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   400.9787   416721.4   645.5396          0       3653      37692 
file results/two/5_age.xlsx saved
transition cell24 age 61

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell24 age 62
durretrisk cell24 age 62

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |       131        131   448.3511   924558.4   961.5396          0       5844      58734 
file results/two/5_age.xlsx saved
transition cell24 age 62

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell24 age 63
durretrisk cell24 age 63

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   389.8659   827247.8   909.5316          0       6302      31969 
file results/two/5_age.xlsx saved
transition cell24 age 63

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell24 age 64
durretrisk cell24 age 64

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   681.8667    1076047   1037.327          0       4222      10228 
file results/two/5_age.xlsx saved
transition cell24 age 64

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
durretrisk cell24 age 65
durretrisk cell24 age 65

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
  durretrisk |        NA         NA   296.4545   192582.3    438.842          0       1187       3261 
file results/two/5_age.xlsx saved
transition cell24 age 65

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_age.xlsx saved
file results/two/5_age.xlsx saved
Job loss rate (by cell - unemployment after lignite only)
(186,939 real changes made)
(1,239,120 missing values generated)
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 1

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      5247       5247          1          0          0          1          1       5247 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 1

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      6164       6164   900.9661   784064.7   885.4743          1       5075    5553555 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 1

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       705        705   .9943262   .0056496   .0751637          0          1        701 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 2

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      1072       1072          1          0          0          1          1       1072 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 2

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      1164       1164   905.1942   532597.6   729.7928          5       4808    1053646 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 2

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       117        117          1          0          0          1          1        117 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 3

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      5465       5465          1          0          0          1          1       5465 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 3

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      7764       7764   2811.539    8012601   2830.654          1      11963   2.18e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 3

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       578        578   .9775087   .0220236   .1484035          0          1        565 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 4

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      1197       1197          1          0          0          1          1       1197 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 4

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      1812       1812   1180.994    1512133   1229.688          1       8544    2139962 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 4

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       105        105          1          0          0          1          1        105 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 5

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      7519       7519          1          0          0          1          1       7519 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 5

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |     10065      10065   2772.186    6517147    2552.87          0      12053   2.79e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 5

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       161        161   .9751553   .0243789   .1561374          0          1        157 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 6

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      2302       2302          1          0          0          1          1       2302 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 6

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      3237       3237   2103.128    2822252   1679.956          0       9088    6807826 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 6

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 7

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |     13855      13855          1          0          0          1          1      13855 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 7

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |     16989      16989    741.292   551586.4   742.6886          1       4897   1.26e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 7

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      4341       4341   .9951624   .0048153   .0693924          0          1       4320 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 8

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      2351       2351          1          0          0          1          1       2351 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 8

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      2767       2767   753.3553     442135   664.9324          1       4077    2084534 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 8

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       211        211          1          0          0          1          1        211 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 9

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |     30856      30856          1          0          0          1          1      30856 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 9

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |     42802      42802   1959.145    5071185   2251.929          1      11688   8.39e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 9

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      8655       8655   .9938764   .0060868   .0780182          0          1       8602 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 10

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      4357       4357          1          0          0          1          1       4357 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 10

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      5727       5727   1461.437    2060246   1435.356          1      10012    8369648 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 10

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       293        293          1          0          0          1          1        293 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 11

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |     31204      31204          1          0          0          1          1      31204 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 11

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |     40462      40462   2577.484    6798734   2607.438          0      14513   1.04e+08 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 11

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      2792       2792    .989255   .0106333   .1031181          0          1       2762 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 12

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      8947       8947          1          0          0          1          1       8947 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 12

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |     12387      12387   1602.654    2364402   1537.661          0      13408   1.99e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 12

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       179        179          1          0          0          1          1        179 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 13

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       829        829          1          0          0          1          1        829 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 13

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       941        941   762.6525   689999.5   830.6621          1       5290     717656 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 13

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       271        271     .99631     .00369   .0607457          0          1        270 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 14

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       111        111          1          0          0          1          1        111 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 14

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       122        122   794.5984   401002.9   633.2479         25       3378      96941 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 14

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 15

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      1073       1073          1          0          0          1          1       1073 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 15

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      1359       1359    1075.24    2560699   1600.218          1      11323    1461251 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 15

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       432        432          1          0          0          1          1        432 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 16

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 16

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       138        138   734.3406    1069719   1034.272          1       6243     101339 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 16

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 17

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      1245       1245          1          0          0          1          1       1245 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 17

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      1585       1585   1735.789    4029319   2007.316          0      11688    2751226 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 17

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       145        145   .9931034   .0068966   .0830455          0          1        144 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 18

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 18

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       158        158   1138.025    1963813   1401.361          0       6209     179808 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 18

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 19

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      4622       4622          1          0          0          1          1       4622 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 19

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      5594       5594    606.458   442060.7   664.8765          1       4662    3392526 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 19

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      2018       2018   .9985134   .0014851   .0385376          0          1       2015 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 20

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       386        386          1          0          0          1          1        386 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 20

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       465        465   621.7484   314172.6    560.511          2       3621     289113 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 20

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 21

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      9249       9249          1          0          0          1          1       9249 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 21

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |     12028      12028   1065.085    1717197   1310.419          1      11323   1.28e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 21

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      4224       4224   .9988163   .0011826   .0343888          0          1       4219 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 22

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       651        651          1          0          0          1          1        651 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 22

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       831        831   1080.789    1166682   1080.131          5       6575     898136 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 22

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 23

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      5795       5795          1          0          0          1          1       5795 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 23

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      7348       7348    1836.05    4342810   2083.941          0      12053   1.35e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 23

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       759        759    .997365   .0026316   .0512988          0          1        757 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 24

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       848        848          1          0          0          1          1        848 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 24

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      1271       1271    939.144    1696472   1302.487          0       9101    1193652 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 24

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
Job loss rate (by cell -unemployment after nonlignite only)
(461,895 real changes made)
(1,183,851 missing values generated)
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 1

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      3307       3307          1          0          0          1          1       3307 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 1

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      5683       5683   417.9611   392071.8   626.1563          1       5265    2375273 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 1

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      1146       1146   .9982548   .0017437   .0417574          0          1       1144 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 2

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      1789       1789          1          0          0          1          1       1789 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 2

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      2905       2905   323.6936   357983.5   598.3172          1       4723     940330 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 2

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       395        395   .9974684   .0025316   .0503155          0          1        394 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 3

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      2070       2070          1          0          0          1          1       2070 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 3

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      3426       3426   1207.646    2679159   1636.814          1      11372    4137394 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 3

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      1095       1095   .9835616   .0161829   .1272121          0          1       1077 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 4

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       876        876          1          0          0          1          1        876 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 4

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      1474       1474   912.0041    2178035   1475.817          1       9802    1344294 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 4

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       428        428   .9976636   .0023364   .0483368          0          1        427 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 5

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      1501       1501          1          0          0          1          1       1501 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 5

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      2277       2277    1573.76    3249735   1802.702          0      10592    3583452 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 5

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       252        252   .9920635   .0079049   .0889094          0          1        250 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 6

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       679        679          1          0          0          1          1        679 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 6

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      1153       1153   1498.716    3121326   1766.727          0      13283    1728020 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 6

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       191        191          1          0          0          1          1        191 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 7

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |     18210      18210          1          0          0          1          1      18210 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 7

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |     45273      45273   430.5774   288903.7   537.4977          1       4749   1.95e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 7

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |     19939      19939   .9920758   .0078618   .0886666          0          1      19781 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 8

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      9353       9353          1          0          0          1          1       9353 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 8

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |     18976      18976   423.7043   279878.3   529.0353          1       4277    8040213 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 8

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      5652       5652   .9992923   .0007073   .0265958          0          1       5648 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 9

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |     38416      38416          1          0          0          1          1      38416 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 9

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |    120821     120821   843.5388    1406642   1186.019          0      11459   1.02e+08 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 9

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |     65702      65702   .9909135   .0090041   .0948897          0          1      65105 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 10

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |     24712      24712          1          0          0          1          1      24712 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 10

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |     57925      57925   965.0934    1650395   1284.677          1      12744   5.59e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 10

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |     16289      16289   .9930628   .0068895    .083003          0          1      16176 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 11

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |     26514      26514          1          0          0          1          1      26514 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 11

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |     60606      60606   1101.168    2387541   1545.167          0      11495   6.67e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 11

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |     22841      22841   .9802548   .0193562   .1391265          0          1      22390 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 12

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |     24604      24604          1          0          0          1          1      24604 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 12

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |     43511      43511   1148.904    2520709   1587.674          0      14334   5.00e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 12

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      6940       6940   .9873199   .0125211   .1118979          0          1       6852 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 13

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       501        501          1          0          0          1          1        501 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 13

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       688        688   388.4506   301879.3   549.4354          1       3393     267254 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 13

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       188        188   .9787234   .0209353   .1446902          0          1        184 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 14

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       267        267          1          0          0          1          1        267 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 14

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       383        383    310.188   170926.2   413.4323          1       3116     118802 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 14

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 15

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       773        773          1          0          0          1          1        773 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 15

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      1218       1218   726.4483   873036.7   934.3643          1       7577     884814 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 15

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       545        545          1          0          0          1          1        545 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 16

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       196        196          1          0          0          1          1        196 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 16

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       300        300   818.5167    1188786   1090.315          2       7577     245555 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 16

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 17

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       480        480          1          0          0          1          1        480 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 17

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       766        766   902.0522    1693424   1301.316          0       8766     690972 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 17

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       211        211   .9952607   .0047393   .0688428          0          1        210 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 18

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       152        152          1          0          0          1          1        152 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 18

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       255        255     1286.6    3365760   1834.601          0       8931     328083 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 18

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 19

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      4723       4723          1          0          0          1          1       4723 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 19

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      9378       9378   494.6228   307171.9   554.2309          1       4333    4638573 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 19

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      3940       3940   .9898477   .0100518   .1002585          0          1       3900 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 20

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      1675       1675          1          0          0          1          1       1675 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 20

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      3307       3307   515.9858   333593.5   577.5756          1       3560    1706365 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 20

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       928        928          1          0          0          1          1        928 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 21

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |     13837      13837          1          0          0          1          1      13837 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 21

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |     38727      38727   758.6626    1130657   1063.324          1      10046   2.94e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 21

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |     20169      20169   .9886459   .0112257   .1059514          0          1      19940 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 22

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      6169       6169          1          0          0          1          1       6169 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 22

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |     12813      12813   820.3752    1039385   1019.502          1       9072   1.05e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 22

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      3109       3109          1          0          0          1          1       3109 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 23

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      9493       9493          1          0          0          1          1       9493 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 23

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |     18932      18932   1141.238    2552219   1597.567          0      10390   2.16e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 23

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      6238       6238   .9650529   .0337312   .1836606          0          1       6020 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 24

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      7532       7532          1          0          0          1          1       7532 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 24

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |     12287      12287   960.2345    1512054   1229.656          0      11858   1.18e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 24

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      1707       1707          1          0          0          1          1       1707 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
Lignite leavers' Job finding rate (ending in a new lignite job)
(40,479 real changes made)
(1,344,011 missing values generated)
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 1

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 1

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |        NA         NA   156.2295   42383.88   205.8735          1       1461       9530 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 1

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 2

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       551        551          1          0          0          1          1        551 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 2

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       557        557   260.9892   110344.5   332.1814          1       3136     145371 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 2

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 3

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       118        118          1          0          0          1          1        118 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 3

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       122        122   197.5574   80487.57   283.7033          1       2557      24102 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 3

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 4

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       740        740          1          0          0          1          1        740 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 4

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       756        756     483.75   348130.2   590.0256          1       6777     365715 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 4

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 5

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       354        354          1          0          0          1          1        354 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 5

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       362        362   166.4724    47595.5   218.1639          0       2666      60263 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 5

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 6

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       698        698          1          0          0          1          1        698 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 6

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       711        711   1107.767    1524844   1234.846          0       8928     787622 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 6

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 7

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       161        161          1          0          0          1          1        161 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 7

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       163        163   157.7239   41925.36   204.7568          1       1418      25709 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 7

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 8

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      5136       5136          1          0          0          1          1       5136 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 8

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      5320       5320   290.3827   105507.6   324.8194          1       3617    1544836 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 8

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       446        446          1          0          0          1          1        446 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 9

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       492        492          1          0          0          1          1        492 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 9

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       502        502    172.241   42224.53   205.4861          1       2171      86465 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 9

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 10

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      9896       9896          1          0          0          1          1       9896 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 10

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |     10732      10732   427.2487   272748.3   522.2531          1       7152    4585233 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 10

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      1293       1293          1          0          0          1          1       1293 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 11

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       883        883          1          0          0          1          1        883 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 11

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       932        932   217.1942   162084.3   402.5969          0       5114     202425 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 11

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA   .9882353   .0117647   .1084652          0          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 12

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      6853       6853          1          0          0          1          1       6853 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 12

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      7388       7388   868.7202    1120911   1058.731          0       9232    6418105 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 12

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       845        845          1          0          0          1          1        845 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 13

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 13

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |        NA         NA      198.5   25947.39   161.0819         32        530       1985 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 13

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          .          .          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 14

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       176        176          1          0          0          1          1        176 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 14

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       187        187   448.2299   234057.2   483.7946          1       2808      83819 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 14

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 15

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 15

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |        NA         NA   266.3636   73963.39   271.9621          3       1126       5860 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 15

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1         NA         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 16

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       566        566          1          0          0          1          1        566 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 16

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       628        628   728.5127   738274.3   859.2289          1       9334     457506 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 16

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 17

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 17

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |        NA         NA   143.0294   10265.36   101.3181          2        365       4863 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 17

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 18

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       439        439          1          0          0          1          1        439 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 18

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       462        462   1107.621    1782067   1334.941          0       9385     511721 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 18

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 19

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       136        136          1          0          0          1          1        136 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 19

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       136        136   274.1912   47906.84   218.8763          2       1254      37290 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 19

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 20

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      2229       2229          1          0          0          1          1       2229 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 20

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      2316       2316    478.769     206720   454.6647          1       3825    1108829 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 20

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       160        160          1          0          0          1          1        160 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 21

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       169        169          1          0          0          1          1        169 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 21

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       171        171   198.4737   64911.69   254.7777          2       2751      33939 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 21

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 22

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      5683       5683          1          0          0          1          1       5683 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 22

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      5961       5961   815.1969   684012.6   827.0505          1       9223    4859389 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 22

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       409        409          1          0          0          1          1        409 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 23

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 23

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       105        105   264.5429     312892   559.3675          0       4017      27777 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 23

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 24

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      2430       2430          1          0          0          1          1       2430 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 24

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      2481       2481   1157.878    1805164   1343.564          0       9406    2872696 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 24

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       123        123          1          0          0          1          1        123 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
Lignite leavers' Job finding rate (ending in a new non-lignite job)
transition cell 1

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 2

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       373        373          1          0          0          1          1        373 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 3

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 4

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       508        508          1          0          0          1          1        508 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 5

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 6

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       165        165          1          0          0          1          1        165 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 7

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 8

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      3749       3749          1          0          0          1          1       3749 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 9

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 10

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      7691       7691          1          0          0          1          1       7691 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 11

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 12

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      2559       2559          1          0          0          1          1       2559 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 13
no observations
transition cell 14

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       105        105          1          0          0          1          1        105 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 15

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          .          .          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 16

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       343        343          1          0          0          1          1        343 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 17
no observations
transition cell 18

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       172        172          1          0          0          1          1        172 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 19

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 20

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      1389       1389          1          0          0          1          1       1389 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 21

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 22

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      4029       4029          1          0          0          1          1       4029 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 23

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 24

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      1037       1037          1          0          0          1          1       1037 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
Non Lignite leavers' Job finding rate (ending in a new non-lignite job)
(227,573 real changes made)
(1,285,932 missing values generated)
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 1

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       130        130          1          0          0          1          1        130 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 1

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       134        134   145.0746   38608.45   196.4903          1       1250      19440 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 1

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA   .9772727   .0227273   .1507557          0          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 2

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      1138       1138          1          0          0          1          1       1138 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 2

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      1685       1685   168.7638   58855.85   242.6022          1       2144     284367 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 2

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      1152       1152          1          0          0          1          1       1152 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 3

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 3

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |        NA         NA   185.2258   113906.1   337.4998          1       2780      17226 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 3

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA     .96875     .03125   .1767767          0          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 4

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       950        950          1          0          0          1          1        950 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 4

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      1517       1517   365.4759   300524.6   548.2012          1       5990     554427 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 4

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      1165       1165          1          0          0          1          1       1165 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 5

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       100        100          1          0          0          1          1        100 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 5

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       104        104   242.8077   134279.9   366.4423          7       2496      25252 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 5

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 6

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       745        745          1          0          0          1          1        745 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 6

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      1012       1012   809.4585    1174022   1083.523          0       7428     819172 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 6

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       423        423          1          0          0          1          1        423 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 7

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      1011       1011          1          0          0          1          1       1011 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 7

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      1065       1065   148.3728   35627.93   188.7536          1       1650     158017 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 7

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       466        466   .9914163   .0085283   .0923489          0          1        462 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 8

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |     12279      12279          1          0          0          1          1      12279 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 8

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |     26796      26796   167.1941   49004.04   221.3686          1       2855    4480133 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 8

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |     21816      21816   .9999542   .0000458   .0067704          0          1      21815 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 9

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      2358       2358          1          0          0          1          1       2358 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 9

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      2714       2714   217.9974   165481.8   406.7946          1       5746     591645 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 9

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      1636       1636   .9841076   .0156494   .1250976          0          1       1610 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 10

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |     28534      28534          1          0          0          1          1      28534 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 10

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |     89222      89222    283.284   236192.1    485.996          0       7863   2.53e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 10

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |     76719      76719   .9999609   .0000391   .0062532          0          1      76716 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 11

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      1917       1917          1          0          0          1          1       1917 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 11

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      2187       2187   348.5816   374646.6   612.0838          0       6362     762348 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 11

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       761        761   .9579501   .0403347   .2008351          0          1        729 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 12

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |     22239      22239          1          0          0          1          1      22239 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 12

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |     50184      50184   616.2882     993942   996.9664          0       9994   3.09e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 12

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |     31400      31400   .9998726   .0001274   .0112861          0          1      31396 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 13

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 13

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |        NA         NA   246.6957   59032.49    242.966          6        914       5674 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 13

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 14

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       177        177          1          0          0          1          1        177 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 14

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       254        254   241.3583   116833.1   341.8086          1       2493      61305 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 14

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       160        160          1          0          0          1          1        160 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 15

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 15

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |        NA         NA   286.7391   126015.9   354.9872         12       1156       6595 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 15

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 16

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       432        432          1          0          0          1          1        432 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 16

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       599        599   573.9499   700737.9   837.1009          1       7590     343796 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 16

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       449        449          1          0          0          1          1        449 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 17

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 17

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |        NA         NA      803.5    1365908   1168.721          1       4395      19284 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 17

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |        NA         NA          1          0          0          1          1         NA 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 18

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       414        414          1          0          0          1          1        414 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 18

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       593        593   1111.975    1868539   1366.945          1       8443     659401 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 18

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       283        283          1          0          0          1          1        283 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 19

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       269        269          1          0          0          1          1        269 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 19

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |       277        277    200.769   58484.85   241.8364          1       1910      55613 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 19

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       101        101    .980198    .019604   .1400141          0          1         99 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 20

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      2927       2927          1          0          0          1          1       2927 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 20

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      5054       5054   258.1622   99786.93   315.8907          1       2608    1304752 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 20

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      3747       3747          1          0          0          1          1       3747 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 21

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      1203       1203          1          0          0          1          1       1203 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 21

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      1441       1441   331.7203     233512   483.2308          1       4910     478009 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 21

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       937        937   .9679829   .0310251   .1761394          0          1        907 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 22

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |     10396      10396          1          0          0          1          1      10396 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 22

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |     25946      25946   486.3624     501633   708.2606          1       7893   1.26e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 22

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |     21181      21181          1          0          0          1          1      21181 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 23

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |       913        913          1          0          0          1          1        913 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 23

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |      1081       1081   611.6605   807471.4   898.5941          0       6028     661205 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 23

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |       492        492   .9329268   .0627018   .2504033          0          1        459 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
distinct cell 24

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
countinfop~s |      8077       8077          1          0          0          1          1       8077 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
durrisk cell 24

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
     durrisk |     15615      15615   910.4966    1646195   1283.041          0       8747   1.42e+07 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
transition cell 24

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
         end |      8886       8886          1          0          0          1          1       8886 
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
file results/two/5_sample2_wholesampleAndcells_estimates_Censoring.xlsx saved
r; t=991.96 15:10:07

. clear all
r; t=0.07 15:10:07

. 
. 
. /* ------------------------------------------------------------------------ */
.  *  (9) Wage offer distribution
. /* ------------------------------------------------------------------------ */
. 
.         /* After transitioning out of lignite, the model assumes that individuals have 
>         a period of unemployment before finding a new job. 
> 
>    We are interested in two wages:
>         - wc (variable wcoal) is the wage that was paid in coal (= wage before transition to unemployment)
>         - w' (variable woffer) is the wage paid in next job ( = wage after unemployment)
> 
> * (9.0) Reload data before collating spells (USE PRECOLL.DTA) 
>                 * KEEP only spells with info on wage at the beginning or at the end of the spell
>                 * Define CELLS again, with different point in time for macro condition depending on spell in lignite or not (MACRO_WAGE)
> -> SAVE WAGE_SAMPLE.DTA
> * (9.1) average wc by cell (wcoal) - characterize distrbution (normal and lognormal)
> * (9.2) average w' by cell (woffer) - characterize distrbution (normal and lognormal)
>  
> * (9.3) We then assume w' is a function of wc and estimate for each cell
> * a simple linear relationship using different specifications: 
> * - reg w' wc i.year
> * - reg w' wc i.decades
> * - reg w' wc  year
> * - reg w' wc decades
> 
> * (9.4) We are then able to do back of the envelope calculations for the expected 
> * value of continued employment, using the values estimated for rho (ex-mu), delta‚ lambda
> * and some additional assumptions:
> * - b = 0.5 wc
> * - deltaB = 0
> *
> * (9.5) Elements for JAERE revision
> * (9.5.1) Evolution of coal versus non-coal wages over time
> * (9.5.2) Distribution of first age of working in data
> * (9.5.3) wage growth profiles of workers in different age ranges - by age ranges
> */
. 
. *********************************************************************************
. 
. * Reload data before collating spells and define cells again
. * we need to reload the data since we have removed first of consecutive employment spells at start of (6)
. use ${data}\precoll.dta, clear
r; t=22.07 15:10:29

. 
. *(9.0) Prepare
. tab statsimple if tentg_end==. & tentg_beg==.

        0 - |
unemployed, |
 margemp or |
 ALMP / 1 - |
 employed / |
        2 - |
 vocational |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |    219,002       99.47       99.47
          1 |      1,029        0.47       99.94
          2 |        128        0.06      100.00
------------+-----------------------------------
      Total |    220,159      100.00
r; t=0.35 15:10:29

. tab statsimple if tentg_end==. & tentg_beg!=.

        0 - |
unemployed, |
 margemp or |
 ALMP / 1 - |
 employed / |
        2 - |
 vocational |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     22,342       97.50       97.50
          1 |        574        2.50      100.00
------------+-----------------------------------
      Total |     22,916      100.00
r; t=0.39 15:10:30

. tab statsimple if tentg_end!=. & tentg_beg==. 

        0 - |
unemployed, |
 margemp or |
 ALMP / 1 - |
 employed / |
        2 - |
 vocational |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     31,579       98.80       98.80
          1 |        385        1.20      100.00
------------+-----------------------------------
      Total |     31,964      100.00
r; t=0.36 15:10:30

. 
. 
. * KEEP only spells with info on wage at the beginning or at the end of the spell
. keep if (tentg_end!=. | tentg_beg!=.)
(428,502 observations deleted)
r; t=0.83 15:10:31

. 
. * We create cells again
. 
. * For macro condition we consider different point in time
. * For spells in lignite: we are interested in the wage at the end of the spell, so we allocate spells in cell depending on macro situation at the end of the spell
. * For spells not in lignite : we are interested in the wage at the beginning of the spell, so we allocate spells in cell depenending on macro situation at the beginning of the spell* Definition of m
> acro scenario
. 
.                 * bad macro conditions = 1 / good macro conditions = 2
.                 * approximations here
.                 * (1) good macro is less than 10% unemployment
.                 * (2)   Lausitz:    from (incl) 2015-
.                 *               Rheinisches from (incl) 2007 -
.                 *       Helmstedter from (incl) 2008 - 
.                 *               Mitteldeutsches from (incl) 2016 -
.                 
.         * Macro condition at end of spell
.                 cap drop macro_end
r; t=0.00 15:10:31

.                 g macro_end=.
(1,027,536 missing values generated)
r; t=0.02 15:10:31

. 
.                 replace macro_end=1 if mining_area== 1 & endepi<mdy(01,01,2015)
(263,263 real changes made)
r; t=0.04 15:10:31

.                 replace macro_end=1 if mining_area== 2 & endepi<mdy(01,01,2016)
(183,522 real changes made)
r; t=0.06 15:10:31

.                 replace macro_end=1 if mining_area== 3 & endepi<mdy(01,01,2008)
(18,673 real changes made)
r; t=0.05 15:10:31

.                 replace macro_end=1 if mining_area== 4 & endepi<mdy(01,01,2007)
(134,413 real changes made)
r; t=0.05 15:10:31

.                 
.                 replace macro_end=2 if mining_area== 1 & endepi>=mdy(01,01,2015)
(31,417 real changes made)
r; t=0.05 15:10:31

.                 replace macro_end=2 if mining_area== 2 & endepi>=mdy(01,01,2016)
(17,643 real changes made)
r; t=0.05 15:10:31

.                 replace macro_end=2 if mining_area== 3 & endepi>=mdy(01,01,2008)
(4,656 real changes made)
r; t=0.04 15:10:31

.                 replace macro_end=2 if mining_area== 4 & endepi>=mdy(01,01,2007)
(44,220 real changes made)
r; t=0.04 15:10:31

. 
.                 * West-Germany: Unemployment below 10% in all years since 1990 
.                 replace macro_end=2 if (mining_area==. | mining_area==5) & ao_bula<12
(285,737 real changes made)
r; t=0.06 15:10:32

. 
.                 * East Germany: Unemployment below 10% for women since 2012, for men since 2015 
.                 replace macro_end=1 if (mining_area==. | mining_area==5) & ao_bula>=12 & endepi<mdy(01,01,2012) & frau==1
(6,318 real changes made)
r; t=0.06 15:10:32

.                 replace macro_end=1 if (mining_area==. | mining_area==5) & ao_bula>=12 & endepi<mdy(01,01,2014) & frau==0
(28,948 real changes made)
r; t=0.08 15:10:32

.                 replace macro_end=2 if (mining_area==. | mining_area==5) & ao_bula>=12 & endepi>=mdy(01,01,2012) & frau==1
(2,587 real changes made)
r; t=0.07 15:10:32

.                 replace macro_end=2 if (mining_area==. | mining_area==5) & ao_bula>=12 & endepi>=mdy(01,01,2014) & frau==0
(6,139 real changes made)
r; t=0.07 15:10:32

. 
.                 if ${sample}==7{
.                 replace macro_end=1 if endepi<mdy(01,01,2000)
r; t=0.00 15:10:32
.                 replace macro_end=2 if endepi>=mdy(01,01,2000)
r; t=0.00 15:10:32
.                 }
r; t=0.00 15:10:32

.                 
.                 tab macro_end, m

  macro_end |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |    635,137       61.81       61.81
          2 |    392,399       38.19      100.00
------------+-----------------------------------
      Total |  1,027,536      100.00
r; t=0.17 15:10:32

.                 
.                 cap drop macro_beg
r; t=0.00 15:10:32

.                 g macro_beg=.
(1,027,536 missing values generated)
r; t=0.03 15:10:32

. 
.                 replace macro_beg=1 if mining_area== 1 & begepi<mdy(01,01,2015)
(280,485 real changes made)
r; t=0.04 15:10:32

.                 replace macro_beg=1 if mining_area== 2 & begepi<mdy(01,01,2016)
(195,053 real changes made)
r; t=0.05 15:10:32

.                 replace macro_beg=1 if mining_area== 3 & begepi<mdy(01,01,2008)
(19,855 real changes made)
r; t=0.04 15:10:32

.                 replace macro_beg=1 if mining_area== 4 & begepi<mdy(01,01,2007)
(145,394 real changes made)
r; t=0.05 15:10:32

.                 
.                 replace macro_beg=2 if mining_area== 1 & begepi>=mdy(01,01,2015)
(14,195 real changes made)
r; t=0.05 15:10:32

.                 replace macro_beg=2 if mining_area== 2 & begepi>=mdy(01,01,2016)
(6,112 real changes made)
r; t=0.03 15:10:32

.                 replace macro_beg=2 if mining_area== 3 & begepi>=mdy(01,01,2008)
(3,474 real changes made)
r; t=0.05 15:10:32

.                 replace macro_beg=2 if mining_area== 4 & begepi>=mdy(01,01,2007)
(33,239 real changes made)
r; t=0.06 15:10:32

. 
.                 * West-Germany: Unemployment below 10% in all years since 1990 
.                 replace macro_beg=2 if (mining_area==. | mining_area==5) & ao_bula<12
(285,737 real changes made)
r; t=0.07 15:10:32

. 
.                 * East Germany: Unemployment below 10% for women since 2012, for men since 2015 
.                 replace macro_beg=1 if (mining_area==. | mining_area==5) & ao_bula>=12 & begepi<mdy(01,01,2012) & frau==1
(7,000 real changes made)
r; t=0.07 15:10:33

.                 replace macro_beg=1 if (mining_area==. | mining_area==5) & ao_bula>=12 & begepi<mdy(01,01,2014) & frau==0
(31,492 real changes made)
r; t=0.07 15:10:33

.                 replace macro_beg=2 if (mining_area==. | mining_area==5) & ao_bula>=12 & begepi>=mdy(01,01,2012) & frau==1
(1,905 real changes made)
r; t=0.09 15:10:33

.                 replace macro_beg=2 if (mining_area==. | mining_area==5) & ao_bula>=12 & begepi>=mdy(01,01,2014) & frau==0              
(3,595 real changes made)
r; t=0.08 15:10:33

.                 
.                 if ${sample}==7{
.                 replace macro_beg=1 if endepi<mdy(01,01,2000)
r; t=0.00 15:10:33
.                 replace macro_beg=2 if endepi>=mdy(01,01,2000)
r; t=0.00 15:10:33
.                 }
r; t=0.01 15:10:33

.                 
.         * Macro condition at beginning of spell
.                 tab macro_beg, m

  macro_beg |      Freq.     Percent        Cum.
------------+-----------------------------------
          1 |    679,279       66.11       66.11
          2 |    348,257       33.89      100.00
------------+-----------------------------------
      Total |  1,027,536      100.00
r; t=0.17 15:10:33

.                 label define macroLAB 1 "hi-unemp" 2 "lo-unemp"
r; t=0.00 15:10:33

.                 
.                 label values macro_end macroLAB
r; t=0.00 15:10:33

.                 label values macro_beg macroLAB
r; t=0.00 15:10:33

.                 
.         * Macro condition kept to allocate spell in cells
.                 gen macro_wage=.
(1,027,536 missing values generated)
r; t=0.04 15:10:33

.                 replace macro_wage= macro_end if thisspelllignite==1 // we consider macro ondition at end of spell for lignite spells
(306,859 real changes made)
r; t=0.05 15:10:33

.                 replace macro_wage= macro_beg if thisspelllignite==0 // we consider macro conditions at beginning of spell for non lignite spells
(720,677 real changes made)
r; t=0.06 15:10:33

.                 label values macro_wage macroLAB
r; t=0.00 15:10:33

. 
.                 tab macro_wage, m

 macro_wage |      Freq.     Percent        Cum.
------------+-----------------------------------
   hi-unemp |    664,976       64.72       64.72
   lo-unemp |    362,560       35.28      100.00
------------+-----------------------------------
      Total |  1,027,536      100.00
r; t=0.18 15:10:33

. 
.         /* Cell groups (2*2*3*2 = 24 cells) unless ${sample}==7, then only 2 cells
>         - gender (2 values)
>         - education (2 values)
>         - age category (3 values)
>         - macro conditions (2 values)
> 
>          NB not: sector (3 values); mining area (2 values); tenure; occupations
>         */      
.         
. /* ------------------------------------------------------------------------ */
.  *      CHECK MISSING FOR CHARACTERISTICS DEFINING CELLS
. /* ------------------------------------------------------------------------ */
. * All cells
. tab ageend, m

 age at end |
   of spell |      Freq.     Percent        Cum.
------------+-----------------------------------
         18 |     20,956        2.04        2.04
         19 |     26,584        2.59        4.63
         20 |     30,153        2.93        7.56
         21 |     27,856        2.71       10.27
         22 |     22,564        2.20       12.47
         23 |     20,092        1.96       14.42
         24 |     18,764        1.83       16.25
         25 |     18,500        1.80       18.05
         26 |     18,539        1.80       19.85
         27 |     19,222        1.87       21.72
         28 |     19,332        1.88       23.61
         29 |     19,618        1.91       25.52
         30 |     20,186        1.96       27.48
         31 |     20,539        2.00       29.48
         32 |     21,291        2.07       31.55
         33 |     21,663        2.11       33.66
         34 |     22,117        2.15       35.81
         35 |     22,643        2.20       38.02
         36 |     22,778        2.22       40.23
         37 |     22,978        2.24       42.47
         38 |     23,175        2.26       44.72
         39 |     23,228        2.26       46.98
         40 |     23,624        2.30       49.28
         41 |     23,793        2.32       51.60
         42 |     23,817        2.32       53.92
         43 |     24,336        2.37       56.28
         44 |     24,075        2.34       58.63
         45 |     23,995        2.34       60.96
         46 |     23,510        2.29       63.25
         47 |     22,846        2.22       65.47
         48 |     22,784        2.22       67.69
         49 |     22,946        2.23       69.92
         50 |     23,347        2.27       72.20
         51 |     24,536        2.39       74.58
         52 |     24,458        2.38       76.97
         53 |     24,983        2.43       79.40
         54 |     26,340        2.56       81.96
         55 |     32,790        3.19       85.15
         56 |     24,686        2.40       87.55
         57 |     21,591        2.10       89.65
         58 |     18,536        1.80       91.46
         59 |     15,681        1.53       92.98
         60 |     28,023        2.73       95.71
         61 |     11,449        1.11       96.83
         62 |      8,289        0.81       97.63
         63 |      9,051        0.88       98.51
         64 |      3,727        0.36       98.88
         65 |      3,441        0.33       99.21
         66 |      1,868        0.18       99.39
         67 |      1,373        0.13       99.53
         68 |      1,077        0.10       99.63
         69 |        850        0.08       99.71
         70 |        722        0.07       99.78
         71 |        532        0.05       99.84
         72 |        453        0.04       99.88
         73 |        386        0.04       99.92
         74 |        274        0.03       99.94
         75 |        206        0.02       99.96
         76 |        363        0.04      100.00
------------+-----------------------------------
      Total |  1,027,536      100.00
r; t=0.34 15:10:34

. tab agecat2, m          

 age at end |
of spell by |
   category |      Freq.     Percent        Cum.
------------+-----------------------------------
   age18-30 |    282,366       27.48       27.48
   age31-49 |    436,138       42.45       69.92
     age50+ |    309,032       30.08      100.00
------------+-----------------------------------
      Total |  1,027,536      100.00
r; t=0.22 15:10:34

. tab frau, m

       frau |      Freq.     Percent        Cum.
------------+-----------------------------------
       mann |    813,020       79.12       79.12
       frau |    214,516       20.88      100.00
------------+-----------------------------------
      Total |  1,027,536      100.00
r; t=0.18 15:10:34

. tab educ2, m

        education 2 |
         categories |      Freq.     Percent        Cum.
--------------------+-----------------------------------
keine abg. Ausbild. |    150,016       14.60       14.60
    abg. Ausbildung |    855,228       83.23       97.83
                  . |     22,292        2.17      100.00
--------------------+-----------------------------------
              Total |  1,027,536      100.00
r; t=0.21 15:10:34

. tab macro_wage, m       

 macro_wage |      Freq.     Percent        Cum.
------------+-----------------------------------
   hi-unemp |    664,976       64.72       64.72
   lo-unemp |    362,560       35.28      100.00
------------+-----------------------------------
      Total |  1,027,536      100.00
r; t=0.20 15:10:34

. 
. * Only men (cells 1 to 12)
. tab ageend if frau==0, m

 age at end |
   of spell |      Freq.     Percent        Cum.
------------+-----------------------------------
         18 |     18,407        2.26        2.26
         19 |     22,606        2.78        5.04
         20 |     25,496        3.14        8.18
         21 |     23,837        2.93       11.11
         22 |     18,689        2.30       13.41
         23 |     16,604        2.04       15.45
         24 |     15,314        1.88       17.34
         25 |     14,985        1.84       19.18
         26 |     14,818        1.82       21.00
         27 |     15,304        1.88       22.89
         28 |     15,307        1.88       24.77
         29 |     15,319        1.88       26.65
         30 |     15,766        1.94       28.59
         31 |     16,010        1.97       30.56
         32 |     16,563        2.04       32.60
         33 |     16,938        2.08       34.68
         34 |     17,188        2.11       36.80
         35 |     17,560        2.16       38.95
         36 |     17,656        2.17       41.13
         37 |     17,982        2.21       43.34
         38 |     17,977        2.21       45.55
         39 |     17,935        2.21       47.76
         40 |     18,165        2.23       49.99
         41 |     18,380        2.26       52.25
         42 |     18,315        2.25       54.50
         43 |     18,624        2.29       56.79
         44 |     18,358        2.26       59.05
         45 |     18,340        2.26       61.31
         46 |     17,860        2.20       63.50
         47 |     17,437        2.14       65.65
         48 |     17,433        2.14       67.79
         49 |     17,586        2.16       69.96
         50 |     17,730        2.18       72.14
         51 |     18,928        2.33       74.47
         52 |     18,754        2.31       76.77
         53 |     19,303        2.37       79.15
         54 |     20,433        2.51       81.66
         55 |     25,423        3.13       84.79
         56 |     19,451        2.39       87.18
         57 |     17,360        2.14       89.31
         58 |     15,178        1.87       91.18
         59 |     12,906        1.59       92.77
         60 |     22,280        2.74       95.51
         61 |      9,428        1.16       96.67
         62 |      6,749        0.83       97.50
         63 |      7,602        0.94       98.43
         64 |      3,051        0.38       98.81
         65 |      2,851        0.35       99.16
         66 |      1,536        0.19       99.35
         67 |      1,114        0.14       99.49
         68 |        914        0.11       99.60
         69 |        715        0.09       99.69
         70 |        617        0.08       99.76
         71 |        469        0.06       99.82
         72 |        400        0.05       99.87
         73 |        348        0.04       99.91
         74 |        223        0.03       99.94
         75 |        182        0.02       99.96
         76 |        316        0.04      100.00
------------+-----------------------------------
      Total |    813,020      100.00
r; t=0.50 15:10:35

. tab agecat2 if frau==0, m               

 age at end |
of spell by |
   category |      Freq.     Percent        Cum.
------------+-----------------------------------
   age18-30 |    232,452       28.59       28.59
   age31-49 |    336,307       41.37       69.96
     age50+ |    244,261       30.04      100.00
------------+-----------------------------------
      Total |    813,020      100.00
r; t=0.43 15:10:35

. tab educ2 if frau==0, m

        education 2 |
         categories |      Freq.     Percent        Cum.
--------------------+-----------------------------------
keine abg. Ausbild. |    125,939       15.49       15.49
    abg. Ausbildung |    671,688       82.62       98.11
                  . |     15,393        1.89      100.00
--------------------+-----------------------------------
              Total |    813,020      100.00
r; t=0.44 15:10:36

. tab macro_wage if frau==0, m

 macro_wage |      Freq.     Percent        Cum.
------------+-----------------------------------
   hi-unemp |    527,465       64.88       64.88
   lo-unemp |    285,555       35.12      100.00
------------+-----------------------------------
      Total |    813,020      100.00
r; t=0.42 15:10:36

. 
. * New JAERE 26th November 2022  
. cap drop cell
r; t=0.00 15:10:36

. if ${sample}<7{
. egen cell=group(frau educ2 agecat2 macro_wage), label   
(22,292 missing values generated)
r; t=2.35 15:10:39
. global cellnumber "24"  
r; t=0.00 15:10:39
. }
r; t=2.36 15:10:39

. if ${sample}==7{
. egen cell=group(macro_wage), label
r; t=0.00 15:10:39
. global cellnumber "2"
r; t=0.00 15:10:39
. }
r; t=0.00 15:10:39

. if ${sample}==8{
. * JAERE sample for different occupations
. cap drop cell
r; t=0.00 15:10:39
. egen cell=group(beruf12), label
r; t=0.00 15:10:39
. global cellnumber "11"
r; t=0.00 15:10:39
. }
r; t=0.01 15:10:39

. if ${sample}==9{
. * JAERE sample for different regions
. cap drop cell
r; t=0.00 15:10:39
. gen cell=.
r; t=0.00 15:10:39
. * cells 1-4 = four mining areas (1=Lausitz, 2=Mitteld., 3=Helmstedt, 4=Rheinisch, 5=Other) 
. replace cell = mining_area
r; t=0.00 15:10:39
. * cell 5 = West Germany
. replace cell = 5 if (mining_area==. | mining_area==5) & ao_bula < 12
r; t=0.00 15:10:39
. * cell 6 = East Germany
. replace cell = 6 if (mining_area==. | mining_area==5) & (ao_bula>12 & ao_bula<.)
r; t=0.01 15:10:39
. global cellnumber "6"
r; t=0.00 15:10:39
. }
r; t=0.02 15:10:39

. 
. /* ------------------------------------------------------------------------ */
.  *      SAVE AFTER CREATING CELLS -> WAGE_SAMPLE.DTA
. /* ------------------------------------------------------------------------ */  
. 
. if ${sample}<7{
. save ${data}\wage_sample.dta, replace
file \\iab.baintern.de\DFS\017\Ablagen\D01700-Projekte\D01700-COAL\data\wage_sample.dta saved
r; t=34.37 15:11:13
. }
r; t=34.37 15:11:13

. * To avoid writing over wage sample and potentially using 
. * the wrong wage sample in 7biocoal, save using diff names.
. if ${sample}==7{
. save ${data}\wage_sample7.dta, replace
r; t=0.00 15:11:13
. }
r; t=0.01 15:11:13

. if ${sample}==8{
. save ${data}\wage_sample8.dta, replace
r; t=0.00 15:11:13
. }
r; t=0.00 15:11:13

. if ${sample}==9{
. save ${data}\wage_sample9.dta, replace
r; t=0.00 15:11:13
. }
r; t=0.00 15:11:13

. * We have multiple measures of wages - the ones we work with in 
. * article are: for COAL WAGES [wcoal_end_monthly]
. *              for NON-COAL WAGES [wnc_offer_tentg_beg]
. 
. di "Monthly wage by year"
Monthly wage by year
r; t=0.00 15:11:13

. g wage_monthly = tentgelt*tentgeltdays
(31,964 missing values generated)
r; t=0.07 15:11:13

. 
. tab jahrend if wage_monthly!=., matrow(matname) // get years where we have observations

year at end |
   of spell |      Freq.     Percent        Cum.
------------+-----------------------------------
       1975 |      5,703        0.57        0.57
       1976 |      6,802        0.68        1.26
       1977 |     16,628        1.67        2.93
       1978 |      7,516        0.75        3.68
       1979 |      8,558        0.86        4.54
       1980 |      7,418        0.75        5.29
       1981 |      6,712        0.67        5.96
       1982 |      6,620        0.66        6.63
       1983 |      7,082        0.71        7.34
       1984 |      7,839        0.79        8.12
       1985 |      7,715        0.77        8.90
       1986 |      7,151        0.72        9.62
       1987 |      6,449        0.65       10.26
       1988 |      7,094        0.71       10.98
       1989 |      6,996        0.70       11.68
       1990 |      6,809        0.68       12.36
       1991 |     10,956        1.10       13.46
       1992 |     43,704        4.39       17.85
       1993 |     72,825        7.31       25.17
       1994 |     56,654        5.69       30.86
       1995 |     51,603        5.18       36.04
       1996 |     56,225        5.65       41.69
       1997 |     44,529        4.47       46.16
       1998 |     36,153        3.63       49.79
       1999 |     34,000        3.42       53.21
       2000 |     35,117        3.53       56.74
       2001 |     34,656        3.48       60.22
       2002 |     30,412        3.05       63.27
       2003 |     28,454        2.86       66.13
       2004 |     27,904        2.80       68.93
       2005 |     24,692        2.48       71.41
       2006 |     22,708        2.28       73.69
       2007 |     23,227        2.33       76.03
       2008 |     22,658        2.28       78.30
       2009 |     21,451        2.15       80.46
       2010 |     19,401        1.95       82.41
       2011 |     19,247        1.93       84.34
       2012 |     18,829        1.89       86.23
       2013 |     18,116        1.82       88.05
       2014 |     18,139        1.82       89.87
       2015 |     16,888        1.70       91.57
       2016 |     17,239        1.73       93.30
       2017 |     66,071        6.64       99.94
       2018 |        567        0.06       99.99
       2019 |         NA        0.01      100.00
------------+-----------------------------------
      Total |    995,572      100.00
r; t=0.45 15:11:14

. matrix list matname

matname[45,1]
       c1
 r1  1975
 r2  1976
 r3  1977
 r4  1978
 r5  1979
 r6  1980
 r7  1981
 r8  1982
 r9  1983
r10  1984
r11  1985
r12  1986
r13  1987
r14  1988
r15  1989
r16  1990
r17  1991
r18  1992
r19  1993
r20  1994
r21  1995
r22  1996
r23  1997
r24  1998
r25  1999
r26  2000
r27  2001
r28  2002
r29  2003
r30  2004
r31  2005
r32  2006
r33  2007
r34  2008
r35  2009
r36  2010
r37  2011
r38  2012
r39  2013
r40  2014
r41  2015
r42  2016
r43  2017
r44  2018
r45  2019
r; t=0.09 15:11:14

. local dim = rowsof(matname)
r; t=0.00 15:11:14

. display `dim'
45
r; t=0.00 15:11:14

.         
. global year
r; t=0.00 15:11:14

.         
. forvalues i=1/`dim' {
  2.         global year $year matname[`i',1]
  3. }
r; t=0.01 15:11:14

. display $year
197519761977197819791980198119821983198419851986198719881989199019911992199319941995199619971998199920002001200220032004200520062007200820092010201120122013201420152016201720182019
r; t=0.00 15:11:14

.         
. foreach i of global year {
  2.         local j = `i'-1973
  3.         capture noisily estpost sum tentgelt if jahrend==`i'
  4.         if _rc!=2000{
  5.                 putexcel set results/${samplefolder}/5_sample${sample}_wages_distribution, sheet("daily_wage_byyear") modify
  6.                 putexcel A`j'=(`i') B1=("wage_daily") B`j'=matrix(e(mean))
  7.                 `putexcelclose'
  8.         }
  9.         capture noisily estpost sum wage_monthly if jahrend==`i'
 10.         if _rc!=2000{
 11.                 putexcel set results/${samplefolder}/5_sample${sample}_wages_distribution, sheet("monthly_wage_byyear") modify
 12.                 putexcel A`j'=(`i') B1=("wage_monthly") B`j'=matrix(e(mean))
 13.                 `putexcelclose'
 14.         }
 15. }

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |      5703       5703   72.24077   1387.513   37.24934          0   444.2406   411989.1 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |      5703       5703   2199.731    1286506   1134.242          0   13527.13   1.25e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |      6802       6802   76.71025   1364.026   36.93273          0   394.0524   521783.2 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |      6802       6802   2335.827    1264729   1124.602          0   11998.89   1.59e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     16628      16628     84.315   1187.613   34.46177          0   408.5455    1401990 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     16628      16628   2567.392    1101158   1049.361          0   12440.21   4.27e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |      7516       7516   71.63366   1372.711   37.05011          0    565.017   538398.6 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |      7516       7516   2171.007    1295810   1138.336          0   17204.77   1.63e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |      8558       8558   70.70747   1293.807   35.96953          0   305.7971   605114.5 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |      8558       8558   2117.726    1276695   1129.909          0   9311.522   1.81e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |      7418       7418   64.23082   1275.785   35.71814          0   402.9701   476464.2 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |      7418       7418   1893.221    1288369   1135.063          0   12270.44   1.40e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |      6712       6712   61.83088   1340.395    36.6114          0   390.9886   415008.9 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |      6712       6712   1805.659    1360934   1166.591          0    11905.6   1.21e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |      6620       6620   68.54294   1543.398   39.28611          0   406.2081   453754.2 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |      6620       6620   2000.214    1600903   1265.268          0   12369.04   1.32e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |      7082       7082   67.34859   1597.455   39.96818          0   475.3265   476962.7 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |      7082       7082   1970.978    1643065   1281.821          0   14473.69   1.40e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |      7839       7839   67.59842    1891.02   43.48586          0   406.3793     529904 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |      7839       7839   1991.408    1897422   1377.469          0   12374.25   1.56e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |      7715       7715   69.90875   2003.239   44.75755          0   465.8911     539346 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |      7715       7715   2063.955    2006714   1416.585          0   14186.38   1.59e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |      7151       7151   71.09716   2054.415   45.32566          0   366.4755   508415.8 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |      7151       7151   2111.743    2036455   1427.044          0   11159.18   1.51e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |      6449       6449   72.19572   2293.431   47.88978          0   399.7258   465590.2 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |      6449       6449   2151.582    2245894   1498.631          0   12171.65   1.39e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |      7094       7094   76.35906   2303.331   47.99303          0   477.7164   541691.2 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |      7094       7094   2282.591    2250633   1500.211          0   14546.47   1.62e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |      6996       6996   70.32745       2315   48.11444          0   484.4533   492010.8 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |      6996       6996   2102.155    2239885   1496.625          0    14751.6   1.47e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |      6809       6809    74.0692   2479.316   49.79273          0   616.2159   504337.2 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |      6809       6809   2210.586    2414506   1553.868          0   18763.77   1.51e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     10956      10956   66.40724   3976.858   63.06233          0   931.4756   727557.7 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     10956      10956   1977.267    3795388   1948.176          0   28363.43   2.17e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     43704      43704   58.80212   1276.171   35.72354          0   797.3578    2569888 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     43704      43704   1753.822    1257236   1121.265          0   24279.54   7.66e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     72825      72825   61.13492   979.2494   31.29296          0    743.978    4452151 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     72825      72825   1806.846    1016371   1008.152          0   22654.13   1.32e+08 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     56654      56654   55.80508   1119.777   33.46306          0   578.8395    3161581 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     56654      56654   1619.553    1164707   1079.216          0   17625.66   9.18e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     51603      51603   57.93235   1329.307   36.45966          0   812.5541    2989483 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     51603      51603   1677.004    1378504   1174.097          0   24742.27   8.65e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     56225      56225   52.29061   974.5613   31.21796          0   738.8655    2940039 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     56225      56225   1484.744    1046461   1022.967          0   22498.46   8.35e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     44529      44529   59.21042   1222.495   34.96419          0   577.3538    2636581 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     44529      44529   1747.452    1226557     1107.5          0   17580.42   7.78e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     36153      36153   56.80273   1388.465   37.26211          0   890.6357    2053589 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     36153      36153   1701.332    1335258   1155.534          0   27119.86   6.15e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     34000      34000   57.41184   1443.567   37.99431          0   973.7407    1952003 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     34000      34000   1737.672    1357484   1165.111          0    29650.4   5.91e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     35117      35117   59.55885   2107.987   45.91282          0   1819.779    2091528 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     35117      35117   1808.071    1964945   1401.765          0   55412.26   6.35e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     34656      34656   60.02566    2307.53   48.03675          0   1497.986    2080249 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     34656      34656   1824.927    2145029   1464.592          0   45613.67   6.32e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     30412      30412   59.50601   2409.541   49.08708          0   1289.481    1809697 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     30412      30412   1809.917    2237935   1495.973          0   39264.69   5.50e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     28454      28454   61.44427   3071.742    55.4233          0   1534.083    1748335 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     28454      28454   1869.575    2850925   1688.468          0   46712.83   5.32e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     27904      27904   58.54328    3105.35   55.72566          0   2402.723    1633592 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     27904      27904   1781.396    2881602   1697.528          0   73162.92   4.97e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     24692      24692   57.59597   3077.881   55.47866          0   1836.874    1422160 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     24692      24692   1752.739    2855730   1689.891          0   55932.82   4.33e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     22708      22708   53.95555   2934.212   54.16837          0   2095.835    1225223 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     22708      22708   1642.341    2721567   1649.717          0   63818.18   3.73e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     23227      23227   59.23974   4585.858   67.71897          0   1632.687    1375962 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     23227      23227   1803.213    4253201   2062.329          0   49715.33   4.19e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     22658      22658     63.259   6389.104   79.93187          0   2298.595    1433323 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     22658      22658   1925.667    5925197   2434.173          0   69992.21   4.36e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     21451      21451   60.31422   4845.945   69.61282          0   1671.198    1293800 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     21451      21451   1836.053    4494124   2119.935          0   50887.98   3.94e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     19401      19401   54.81672   3571.515   59.76216          0   1570.245    1063499 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     19401      19401   1668.503    3312561   1820.044          0   47813.96   3.24e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     19247      19247   57.39562   4306.765   65.62595          0   2159.611    1104693 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     19247      19247   1747.494    3993616   1998.403          0   65760.15   3.36e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     18829      18829   56.42377   3507.516   59.22429          0   1222.181    1062403 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     18829      18829    1717.85    3252624   1803.503          0   37215.41   3.23e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     18116      18116   59.98284   5260.969   72.53254          0   2137.523    1086649 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     18116      18116   1826.268    4878367   2208.702          0   65087.59   3.31e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     18139      18139   65.22337   7279.095   85.31761          0   2184.627    1183087 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     18139      18139   1985.877    6749619   2598.003          0   66521.89   3.60e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     16888      16888   66.78908   6654.169   81.57309          0   2159.487    1127934 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     16888      16888   2033.514    6170296   2484.008          0   65756.39   3.43e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     17239      17239   72.98908   7470.515   86.43214          0   2591.982    1258259 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     17239      17239    2222.38    6927018   2631.923          0   78925.84   3.83e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |     66071      66071   89.30035   8615.894   92.82184          0   5867.009    5900163 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |     66071      66071   2719.119    7988960   2826.475          0   178650.4   1.80e+08 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |       567        567   13.43873   156.6336   12.51533          0   65.20586    7619.76 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |       567        567   390.3657   131353.6   362.4273          0   1985.518   221337.3 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
    tentgelt |        NA         NA   12.71082   253.0974   15.90903          0   68.13358   699.0952 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wage_monthly |        NA         NA   387.0445   234672.5   484.4301          0   2074.667   21287.45 
file results/two/5_sample2_wages_distribution.xlsx saved
r; t=71.23 15:12:25

. 
. 
. *(9.1) Average wage in lignite before transition to unemployment - Characterize distribution
. * Use pretrans = 7 which indicates the lignite spell in (lignite - unemp - other normalemp not in lignite)
. di "Number of spells of employment in lignite followed by unemployement and then employment not in lignite"
Number of spells of employment in lignite followed by unemployement and then employment not in lignite
r; t=0.00 15:12:25

. count if thisspelllignite == 1 & pretrans == 7
  22,271
r; t=0.03 15:12:25

. 
.         * generate variable that contains lignite income pre-transition
.         cap drop wcoal_suminc_R wcoal_tentg_end wcoal_monthly wcoal_end_monthly
r; t=0.00 15:12:25

.         
.         gen wcoal_suminc_R = .
(1,027,536 missing values generated)
r; t=0.03 15:12:25

.         replace wcoal_suminc_R = suminc_R  if thisspelllignite == 1 & pretrans == 7 //  add additional restrictions ? 
(22,271 real changes made)
r; t=0.05 15:12:25

.         
.         gen wcoal_tentg_end = .
(1,027,536 missing values generated)
r; t=0.03 15:12:25

.         replace wcoal_tentg_end = tentg_end if thisspelllignite == 1 & pretrans == 7 //  add additional restrictions ? 
(22,271 real changes made)
r; t=0.05 15:12:25

. 
.         gen wcoal_end_monthly = .
(1,027,536 missing values generated)
r; t=0.02 15:12:25

.         replace wcoal_end_monthly = tentg_end*tentgeltdays if thisspelllignite == 1 & pretrans == 7 //  add additional restrictions ? 
(22,271 real changes made)
r; t=0.05 15:12:25

.         label var wcoal_end_monthly "monthly wage in coal"
r; t=0.00 15:12:25

. 
.         gen wctentend_preimp = .
(1,027,536 missing values generated)
r; t=0.02 15:12:25

.         replace wctentend_preimp = tentend_preimp if thisspelllignite == 1 & pretrans == 7 //  add additional restrictions ? 
(22,271 real changes made)
r; t=0.04 15:12:25

.         label var wctentend_preimp "daily last wage in coal pre-top-code-impute"
r; t=0.00 15:12:25

. 
.         
.         * full sample - distribution analysis
.         di "Distribution of incomes in lignite before transition to unemployment with suminc_R"
Distribution of incomes in lignite before transition to unemployment with suminc_R
r; t=0.00 15:12:25

.         sum wcoal_suminc_R, d   

                       wcoal_suminc_R
-------------------------------------------------------------
      Percentiles      Smallest
 1%     674.5841       320.4664
 5%     1396.147       332.6837
10%     1540.006       338.4695       Obs              22,271
25%     1741.582       338.4695       Sum of wgt.      22,271

50%     1944.627                      Mean           2122.919
                        Largest       Std. dev.      1069.349
75%     2332.371       32488.24
90%     2863.463       32488.24       Variance        1143508
95%     3354.259       32488.24       Skewness       33.88219
99%     4755.034       92795.85       Kurtosis       2472.252
r; t=0.24 15:12:26

.         
.         di "Distribution of wage in lignite before transition to unemployment with tentg_end daily"
Distribution of wage in lignite before transition to unemployment with tentg_end daily
r; t=0.00 15:12:26

.         estpost sum wcoal_tentg_end, d

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wcoal_tent~d |     22271      22271   72.62358   715.1351   26.74201   6.986502   159.5694    1617400   10.52435   1092.186   25.56212   47.39491   51.55606   57.85632   68.56995   82.24436 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wcoal_tent~d |  97.25443   111.3294   151.8161 
r; t=0.94 15:12:27

. 
.         di "Distribution of wage in lignite before transition to unemployment with tentg_end monthly"
Distribution of wage in lignite before transition to unemployment with tentg_end monthly
r; t=0.00 15:12:27

.         *monthly wage by year
.         tab jahrend if wcoal_end_monthly!=., matrow(matname) // get years where we have observations

year at end |
   of spell |      Freq.     Percent        Cum.
------------+-----------------------------------
       1976 |         NA        0.05        0.05
       1977 |         NA        0.13        0.18
       1978 |         NA        0.04        0.22
       1979 |         NA        0.12        0.34
       1980 |         NA        0.26        0.60
       1981 |         NA        0.17        0.77
       1982 |        317        1.42        2.19
       1983 |        109        0.49        2.68
       1984 |         NA        0.13        2.81
       1985 |         NA        0.15        2.96
       1986 |         NA        0.15        3.12
       1987 |         NA        0.10        3.22
       1988 |         NA        0.17        3.39
       1989 |         NA        0.11        3.50
       1990 |         NA        0.12        3.61
       1991 |         NA        0.28        3.90
       1992 |      5,327       23.92       27.82
       1993 |      4,889       21.95       49.77
       1994 |      1,145        5.14       54.91
       1995 |      2,034        9.13       64.04
       1996 |      2,382       10.70       74.74
       1997 |      1,229        5.52       80.26
       1998 |        784        3.52       83.78
       1999 |        750        3.37       87.14
       2000 |        769        3.45       90.60
       2001 |        358        1.61       92.21
       2002 |        338        1.52       93.72
       2003 |        277        1.24       94.97
       2004 |        200        0.90       95.86
       2005 |        137        0.62       96.48
       2006 |        113        0.51       96.99
       2007 |        140        0.63       97.62
       2008 |         NA        0.41       98.02
       2009 |         NA        0.32       98.34
       2010 |         NA        0.24       98.58
       2011 |         NA        0.28       98.86
       2012 |         NA        0.28       99.14
       2013 |         NA        0.19       99.33
       2014 |         NA        0.25       99.57
       2015 |         NA        0.13       99.70
       2016 |         NA        0.19       99.89
       2017 |         NA        0.11      100.00
------------+-----------------------------------
      Total |     22,271      100.00
r; t=0.23 15:12:27

.         matrix list matname

matname[42,1]
       c1
 r1  1976
 r2  1977
 r3  1978
 r4  1979
 r5  1980
 r6  1981
 r7  1982
 r8  1983
 r9  1984
r10  1985
r11  1986
r12  1987
r13  1988
r14  1989
r15  1990
r16  1991
r17  1992
r18  1993
r19  1994
r20  1995
r21  1996
r22  1997
r23  1998
r24  1999
r25  2000
r26  2001
r27  2002
r28  2003
r29  2004
r30  2005
r31  2006
r32  2007
r33  2008
r34  2009
r35  2010
r36  2011
r37  2012
r38  2013
r39  2014
r40  2015
r41  2016
r42  2017
r; t=0.00 15:12:27

.         local dim = rowsof(matname)
r; t=0.00 15:12:27

.         display `dim'
42
r; t=0.00 15:12:27

.         
.         global year
r; t=0.00 15:12:27

.         forvalues i=1/`dim' {
  2.                 global year $year matname[`i',1]
  3.         }
r; t=0.00 15:12:27

.         display $year
197619771978197919801981198219831984198519861987198819891990199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017
r; t=0.00 15:12:27

.         
.         foreach i of global year {
  2.                 local j = `i'-1973
  3.                 capture noisily estpost sum wcoal_tentg_end if jahrend==`i'
  4.                 if _rc!=2000{
  5.                         putexcel set results/${samplefolder}/5_sample${sample}_wages_distribution, sheet("daily_wage_byyear") modify
  6.                         putexcel A`j'=(`i') C1=("wcoal_end_daily") C`j'=matrix(e(mean))
  7.                         `putexcelclose'
  8.                 }
  9.                 capture noisily estpost sum wcoal_end_monthly if jahrend==`i'
 10.                 if _rc!=2000{
 11.                         putexcel set results/${samplefolder}/5_sample${sample}_wages_distribution, sheet("monthly_wage_byyear") modify
 12.                         putexcel A`j'=(`i') C1=("wcoal_end_monthly") C`j'=matrix(e(mean))
 13.                         `putexcelclose'
 14.                 }
 15.         }

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |        NA         NA   61.70529   1166.177   34.14933   16.75327   103.6466   678.7582 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   1878.926    1081282   1039.847   510.1369   3156.038   20668.19 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |        NA         NA   51.14481   868.8103   29.47559   17.33352   113.2898   1483.199 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   1557.359   805563.1   897.5317   527.8057   3449.673   45163.42 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |        NA         NA   42.57485   643.0291   25.35802   18.23585   86.45419   340.5988 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   1296.404   596218.2   772.1517   555.2815    2632.53   10371.23 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |        NA         NA   57.28643   1404.168   37.47223   18.40736   153.4129   1546.734 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   1744.372    1301948   1141.029   560.5042   4671.423   47098.04 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |        NA         NA   72.27919    847.919   29.11905   14.42873    141.079   4192.193 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   2200.901   786192.7   886.6751   439.3548   4295.855   127652.3 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |        NA         NA   75.36256   2771.166   52.64186   19.39147   291.6662   2863.777 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA    2294.79    2569432   1602.945   590.4703   8881.237   87202.02 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |       317        317   96.94148   716.6058   26.76949   14.22068   293.0018   30730.45 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |       317        317   2951.868   664438.8   815.1311   433.0197   8921.904   935742.2 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |       109        109    107.482   891.3632   29.85571   31.36297    275.314   11715.54 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |       109        109   3272.828   826474.2   909.1063   955.0024   8383.312   356738.2 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |        NA         NA   68.94939   399.6789   19.99197   28.90594   106.2551   1999.532 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   2099.509   370583.3   608.7556    880.186   3235.468   60885.76 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |        NA         NA   89.84314   571.3594   23.90313   22.00064   146.4354   3054.667 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   2735.724   529765.9   727.8502   669.9196   4458.958   93014.61 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |        NA         NA   97.82112   748.5729   27.36006   52.55346    160.652   3325.918 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   2978.653   694078.7   833.1139   1600.253   4891.853   101274.2 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |        NA         NA   93.31568   947.4535   30.78073   43.88192   188.3653   2146.261 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   2841.462   878481.2   937.2733   1336.204   5735.724   65353.64 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |        NA         NA   93.10251   881.2697   29.68619   53.59564   175.4861   3537.896 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   2834.972   817115.5   903.9444   1631.987   5343.551   107728.9 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |        NA         NA   79.79805   156.5018   12.51007   59.27014   108.6852   1915.153 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   2429.851   145108.9   380.9316   1804.776   3309.463   58316.42 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |        NA         NA    91.3222   401.4062   20.03513   54.78129    148.282   2374.377 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   2780.761   372184.9   610.0696    1668.09   4515.187   72299.79 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |        NA         NA   105.8471   434.4388   20.84319   66.74468   176.6843    6668.37 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   3223.046   402812.8   634.6753   2032.376   5380.038   203051.9 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |      5327       5327   62.28219   165.5178   12.86537   10.92557   233.8295   331777.2 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |      5327       5327   1896.493   153468.5   391.7506   332.6837   7120.108   1.01e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |      4889       4889     77.206   223.0738   14.93566   12.81214   311.2928   377460.1 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |      4889       4889   2350.923   206834.6   454.7907   390.1296   9478.867   1.15e+07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |      1145       1145   81.15526   281.2853   16.77156   29.41059   211.3405   92922.77 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |      1145       1145   2471.178   260808.4    510.694   895.5526    6435.32    2829498 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |      2034       2034   73.38097   438.8046   20.94766   10.52435   219.3244   149256.9 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |      2034       2034   2234.451   406860.8   637.8564   320.4664   6678.429    4544873 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |      2382       2382   68.29862   442.9579   21.04657   24.02319   206.8431   162687.3 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |      2382       2382   2079.693   410711.7   640.8679   731.5061   6298.374    4953829 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |      1229       1229   70.33654   595.2913   24.39859   30.36409   311.6318   86443.61 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |      1229       1229   2141.748   551955.6   742.9372   924.5866   9489.188    2632208 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |       784        784   77.29172   970.3166    31.1499   25.49764    431.911   60596.71 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |       784        784   2353.533     899680   948.5146   776.4031   13151.69    1845170 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |       750        750   73.93442   671.1932    25.9074   14.20118   330.5528   55450.81 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |       750        750   2251.303   622332.1   788.8803   432.4261   10065.33    1688477 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |       769        769    62.6786   1157.913   34.02811   14.99416   308.2756   48199.84 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |       769        769   1908.563    1073619   1036.156   456.5723   9386.992    1467685 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |       358        358   67.35278   2305.431    48.0149   14.06178   320.0397   24112.29 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |       358        358   2050.892    2137601   1462.054   428.1811   9745.209   734219.4 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |       338        338    82.1129   2054.208   45.32337   22.33634   509.1165   27754.16 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |       338        338   2500.338    1904667   1380.097   680.1415    15502.6   845114.2 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |       277        277   78.02171   804.3036   28.36025   35.18973   256.9592   21612.01 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |       277        277   2375.761   745752.3   863.5695   1071.527   7824.408   658085.9 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |       200        200   93.30037   3953.897   62.88002    27.1868   721.4448   18660.07 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |       200        200   2840.996    3666063   1914.697   827.8381      21968   568199.3 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |       137        137   72.59219   608.4972   24.66774   38.49728   210.7587    9945.13 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |       137        137   2210.432   564200.1   751.1326   1172.242   6417.602   302829.2 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |       113        113   82.15536   2275.536   47.70257   19.51011   376.5336   9283.556 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |       113        113   2501.631    2109882   1452.543   594.0828   11465.45   282684.3 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |       140        140   73.74285   1214.397   34.84819    21.0822   293.6085      10324 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |       140        140    2245.47    1125992   1061.128   641.9531   8940.379   314365.8 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |        NA         NA   81.28803   2036.716   45.12999   44.26977   340.1127   7397.211 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   2475.221    1888448   1374.208   1348.015   10356.43   225245.1 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |        NA         NA   91.41876   5090.606   71.34848   46.34984   472.4459   6490.732 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   2783.701    4720023   2172.561   1411.353   14385.98   197642.8 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |        NA         NA   91.65133   4185.379   64.69451       20.7   507.4619   4857.521 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   2790.783    3880694   1969.948   630.3149   15452.21   147911.5 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |        NA         NA   84.22949   707.4396   26.59774    36.5426   187.7185   5222.228 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   2564.788   655939.8   809.9011   1112.722   5716.028   159016.9 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |        NA         NA   100.9345   1690.212   41.11219   26.50336   251.0217   6257.939 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   3073.456    1567169   1251.866   807.0273   7643.611   190554.2 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |        NA         NA   120.5581   11100.98   105.3612   32.52599   676.3381   5063.441 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   3670.995   1.03e+07   3208.249   990.4165    20594.5   154181.8 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |        NA         NA   115.8771   22416.77   149.7223    18.0394   1092.186    6373.24 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   3528.458   2.08e+07   4559.044   549.2996   33257.08   194065.2 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |        NA         NA   105.2244   912.9023   30.21427   47.58652   184.4797   3051.507 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   3204.082   846445.3   920.0246    1449.01   5617.406   92918.38 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |        NA         NA   93.29076   1213.359    34.8333     11.527   171.9561   3918.212 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   2840.704    1125030   1060.674   350.9971   5236.063   119309.6 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_tent~d |        NA         NA   154.4887   16408.43   128.0954   60.32021   593.8519   3707.729 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   4704.181   1.52e+07   3900.505    1836.75   18082.79   112900.3 
file results/two/5_sample2_wages_distribution.xlsx saved
r; t=60.33 15:13:27

.         
.         *full sample - normal distribution paramaters
.         capture noisily estpost sum wcoal_end_monthly, d

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wcoal_end_~y |     22271      22271   2211.388   663075.1   814.2942   6.986502   159.5695   4.92e+07   320.4664   33257.08   778.3665   1443.175   1569.882   1761.725   2087.955   2504.341 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wcoal_end_~y |  2961.397    3389.98   4622.802 
r; t=0.88 15:13:28

.         if _rc!=2000{
.                 putexcel set results/${samplefolder}/5_sample${sample}_wages_distribution, sheet("wcoal_normal") modify
r; t=0.01 15:13:28
.                 #delimit ; 
delimiter now ;
.                 putexcel A2=("wcoal_end_monthly_all") B1=("mean") B2=matrix(e(mean)) C1=("sd") C2=matrix(e(sd)) D1=("sum_w") D2=matrix(e(sum_w)) E1=("skewness") E2=matrix(e(skewness)) F1=("kurtosis"
> ) F2=matrix(e(kurtosis)) G1=("sum") G2=matrix(e(sum)) H1=("min") H2=matrix(e(min)) I1=("max") I2=matrix(e(max)) J1=("p1") J2=matrix(e(p1)) K1=("p5") K2=matrix(e(p5)) L1=("p10") L2=matrix(e(p10)) M1=
> ("p25") M2=matrix(e(p25)) N1=("p50") N2=matrix(e(p50)) O1=("p75") O2=matrix(e(p75)) P1=("p90") P2=matrix(e(p90)) Q1=("p95") Q2=matrix(e(p95)) R1=("p99") R2=matrix(e(p99)) ;
file results/two/5_sample2_wages_distribution.xlsx saved
r; t=0.07 15:13:28
.                 #delimit cr
delimiter now cr
.                 `putexcelclose'
r; t=0.00 15:13:28
.         }       
r; t=0.10 15:13:28

.         
.         *full sample - normal distribution fit
.         dpplot wcoal_end_monthly if wcoal_end_monthly<=$incmax, caption("") mlw(medthin) ms(oh) scheme(economist)
r; t=6.06 15:13:34

.         graph export results/${samplefolder}/5_sample${sample}_wcoal_dpplot.pdf, replace
file results/two/5_sample2_wcoal_dpplot.pdf saved as PDF format
r; t=0.65 15:13:35

.         kdensity wcoal_end_monthly, normal 
r; t=3.70 15:13:39

.         graph export results/${samplefolder}/5_sample${sample}_wcoal_kdensity.pdf, replace
file results/two/5_sample2_wcoal_kdensity.pdf saved as PDF format
r; t=0.14 15:13:39

.         qnorm wcoal_end_monthly
r; t=4.13 15:13:43

.         graph export results/${samplefolder}/5_sample${sample}_wcoal_qnorm.pdf, replace
file results/two/5_sample2_wcoal_qnorm.pdf saved as PDF format
r; t=0.82 15:13:44

.         sktest wcoal_end_monthly

Skewness and kurtosis tests for normality
                                                              ----- Joint test -----
         Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
------------------+-----------------------------------------------------------------
wcoal_end_monthly |    22,271         0.0000         0.0000             .          .
r; t=0.43 15:13:44

. 
.         *full sample - lognormal distribution parameters
.         lognfit wcoal_end_monthly, stats

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -2458421.3
rescale:       log likelihood = -231121.55
rescale eq:    log likelihood = -185055.25
Iteration 0:   log likelihood = -185055.25  (not concave)
Iteration 1:   log likelihood = -177044.48  
Iteration 2:   log likelihood = -175838.01  
Iteration 3:   log likelihood = -175789.37  
Iteration 4:   log likelihood = -175789.36  

ML fit of lognormal distribution                  Number of obs   =      22271
                                                  Wald chi2(0)    =          .
Log likelihood = -175789.36                       Prob > chi2     =          .

------------------------------------------------------------------------------
wcoal_end_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.652581   .0020625  3710.37   0.000     7.648538    7.656623
-------------+----------------------------------------------------------------
v            |
       _cons |   .3077945   .0014584   211.05   0.000     .3049361    .3106529
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wcoal_end_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%   1.03e+03       0.00422
 5%   1.27e+03       0.02543
10%   1.42e+03       0.05599
20%   1.63e+03       0.12519
25%   1.71e+03       0.16298
30%   1.79e+03       0.20265
40%   1.95e+03       0.28735 Mode         1.92e+03
50%   2.11e+03       0.37912 Mean         2.21e+03
60%   2.28e+03       0.47829 Std. Dev.   696.10300
70%   2.47e+03       0.58574
75%   2.59e+03       0.64308 Variance     4.85e+05
80%   2.73e+03       0.70327 Half CV^2     0.04969
90%   3.12e+03       0.83491 Gini coeff.   0.17229
95%   3.49e+03       0.90940 p90/p10       2.20099
99%   4.31e+03       0.97823 p75/p25       1.51469
------------------------------------------------------------
r; t=30.94 15:14:15

.         preserve
r; t=0.58 15:14:16

.         keep if e(sample)==1
(1,005,265 observations deleted)
r; t=0.28 15:14:16

.         if ${sample}<7{
.                 save ${data}\wage_sample_wcoal_estimation.dta, replace
file \\iab.baintern.de\DFS\017\Ablagen\D01700-Projekte\D01700-COAL\data\wage_sample_wcoal_estimation.dta saved
r; t=0.82 15:14:17
.         }
r; t=0.83 15:14:17

.         if ${sample}==7{
.                 save ${data}\wage_sample7_wcoal_estimation.dta, replace
r; t=0.00 15:14:17
.         }
r; t=0.00 15:14:17

.         if ${sample}==8{
.                 save ${data}\wage_sample8_wcoal_estimation.dta, replace
r; t=0.00 15:14:17
.         }
r; t=0.00 15:14:17

.         if ${sample}==9{
.                 save ${data}\wage_sample9_wcoal_estimation.dta, replace
r; t=0.00 15:14:17
.         }
r; t=0.00 15:14:17

. 
.         tab pretrans

                               pretrans |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
pre trans'n 2 unemp/ALMP/marg,then non- |     22,271      100.00      100.00
----------------------------------------+-----------------------------------
                                  Total |     22,271      100.00
r; t=0.01 15:14:17

.         restore
r; t=0.08 15:14:17

.         putexcel set results/${samplefolder}/5_sample${sample}_wages_distribution, sheet("wcoal_lognormal") modify
r; t=0.10 15:14:17

.         putexcel A2=("wcoal_end_monthly_all") B1=("mu") B2=(e(bm)) C1=("sigma") C2=(e(bv)) 
file results/two/5_sample2_wages_distribution.xlsx saved
r; t=0.08 15:14:17

.         `putexcelclose'
r; t=0.00 15:14:17
.         
.         *full sample - lognormal distribution fit
.         dpplot wcoal_end_monthly if wcoal_end_monthly<=$incmax, dist(lognormal) param(`e(bm)' `e(bv)') caption("") mlw(medthin) ms(oh) scheme(economist) 
r; t=5.67 15:14:23

.         graph export results/${samplefolder}/5_sample${sample}_wcoal_dpplot_logn.pdf, replace
file results/two/5_sample2_wcoal_dpplot_logn.pdf saved as PDF format
r; t=0.66 15:14:23

.         _pctile wcoal_end_monthly if wcoal_end_monthly<=$incmax, nq(1000)       
r; t=0.25 15:14:24

.         return list

scalars:
                 r(r1) =  510.1369323730469
                 r(r2) =  585.9054565429688
                 r(r3) =  595.0635986328125
                 r(r4) =  601.3350830078125
                 r(r5) =  610.0450439453125
                 r(r6) =  661.8515014648438
                 r(r7) =  732.49658203125
                 r(r8) =  751.2759399414063
                 r(r9) =  771.7017822265625
                r(r10) =  778.3665161132813
                r(r11) =  793.7607421875
                r(r12) =  798.2716674804688
                r(r13) =  807.6177368164063
                r(r14) =  816.1448364257813
                r(r15) =  821.4747924804688
                r(r16) =  832.3912353515625
                r(r17) =  887.4840087890625
                r(r18) =  918.8291625976563
                r(r19) =  940.4235229492188
                r(r20) =  967.8837280273438
               r(r999) =  6769.3330078125
               r(r998) =  6158.02783203125
               r(r997) =  5696.17919921875
               r(r996) =  5416.55859375
               r(r995) =  5202.82763671875
               r(r994) =  4966.84619140625
               r(r993) =  4786.95068359375
               r(r992) =  4654.95654296875
               r(r991) =  4558.82958984375
               r(r990) =  4491.22998046875
               r(r989) =  4439.375
               r(r988) =  4347.43408203125
               r(r987) =  4274.453125
               r(r986) =  4200.46728515625
               r(r985) =  4158.521484375
               r(r984) =  4131.67333984375
               r(r983) =  4092.533935546875
               r(r982) =  4057.9833984375
               r(r981) =  4022.3798828125
               r(r980) =  3999.991455078125
               r(r979) =  3973.202880859375
               r(r978) =  3947.408935546875
               r(r977) =  3903.465087890625
               r(r976) =  3867.1845703125
               r(r975) =  3828.484130859375
               r(r974) =  3809.624755859375
               r(r973) =  3784.940185546875
               r(r972) =  3761.64208984375
               r(r971) =  3736.954833984375
               r(r970) =  3716.740478515625
               r(r969) =  3686.016357421875
               r(r968) =  3663.401123046875
               r(r967) =  3642.300048828125
               r(r966) =  3627.0673828125
               r(r965) =  3605.25439453125
               r(r964) =  3586.135986328125
               r(r963) =  3571.70263671875
               r(r962) =  3554.259765625
               r(r961) =  3537.79248046875
               r(r960) =  3520.06787109375
               r(r959) =  3506.309326171875
               r(r958) =  3494.188720703125
               r(r957) =  3483.551513671875
               r(r956) =  3466.75048828125
               r(r955) =  3449.547607421875
               r(r954) =  3436.705810546875
               r(r953) =  3416.165283203125
               r(r952) =  3395.06591796875
               r(r951) =  3380.1298828125
               r(r950) =  3371.17919921875
               r(r949) =  3358.708251953125
               r(r948) =  3340.09326171875
               r(r947) =  3326.8134765625
               r(r946) =  3316.346435546875
               r(r945) =  3308.427001953125
               r(r944) =  3295.403076171875
               r(r943) =  3286.7001953125
               r(r942) =  3275.560546875
               r(r941) =  3267.838623046875
               r(r940) =  3254.442138671875
               r(r939) =  3243.5078125
               r(r938) =  3233.31396484375
               r(r937) =  3220.78955078125
               r(r936) =  3208.14599609375
               r(r935) =  3198.236572265625
               r(r934) =  3191.055419921875
               r(r933) =  3179.411865234375
               r(r932) =  3172.4462890625
               r(r931) =  3164.60693359375
               r(r930) =  3155.74072265625
               r(r929) =  3149.115478515625
               r(r928) =  3141.8095703125
               r(r927) =  3134.5634765625
               r(r926) =  3125.181884765625
               r(r925) =  3119.26318359375
               r(r924) =  3110.4775390625
               r(r923) =  3102.942138671875
               r(r922) =  3093.54150390625
               r(r921) =  3087.818115234375
               r(r920) =  3079.996826171875
               r(r919) =  3075.309326171875
               r(r918) =  3066.6513671875
               r(r917) =  3062.30810546875
               r(r916) =  3057.48291015625
               r(r915) =  3052.6572265625
               r(r914) =  3047.88134765625
               r(r913) =  3041.627197265625
               r(r912) =  3032.45703125
               r(r911) =  3025.3330078125
               r(r910) =  3018.3056640625
               r(r909) =  3012.19677734375
               r(r908) =  3004.72509765625
               r(r907) =  2998.00048828125
               r(r906) =  2991.27734375
               r(r905) =  2982.522705078125
               r(r904) =  2976.83740234375
               r(r903) =  2970.193115234375
               r(r902) =  2964.342529296875
               r(r901) =  2958.482666015625
               r(r900) =  2953.63525390625
               r(r899) =  2949.68408203125
               r(r898) =  2943.316162109375
               r(r897) =  2937.85400390625
               r(r896) =  2930.83154296875
               r(r895) =  2926.37451171875
               r(r894) =  2921.32666015625
               r(r893) =  2916.279296875
               r(r892) =  2911.36669921875
               r(r891) =  2907.13623046875
               r(r890) =  2901.603759765625
               r(r889) =  2897.59521484375
               r(r888) =  2892.731689453125
               r(r887) =  2888.68310546875
               r(r886) =  2884.8134765625
               r(r885) =  2877.794677734375
               r(r884) =  2871.457763671875
               r(r883) =  2868.977294921875
               r(r882) =  2863.46337890625
               r(r881) =  2858.992919921875
               r(r880) =  2855.2646484375
               r(r879) =  2851.404052734375
               r(r878) =  2845.72265625
               r(r877) =  2841.3623046875
               r(r876) =  2838.212158203125
               r(r875) =  2834.699462890625
               r(r874) =  2831.467041015625
               r(r873) =  2828.23681640625
               r(r872) =  2824.889892578125
               r(r871) =  2820.16650390625
               r(r870) =  2816.1279296875
               r(r869) =  2811.88134765625
               r(r868) =  2809.053955078125
               r(r867) =  2806.131591796875
               r(r866) =  2801.78369140625
               r(r865) =  2798.9560546875
               r(r864) =  2793.916015625
               r(r863) =  2789.472412109375
               r(r862) =  2786.053955078125
               r(r861) =  2782.484619140625
               r(r860) =  2779.51953125
               r(r859) =  2776.408935546875
               r(r858) =  2773.290771484375
               r(r857) =  2769.7626953125
               r(r856) =  2766.062744140625
               r(r855) =  2761.6376953125
               r(r854) =  2757.5205078125
               r(r853) =  2754.5517578125
               r(r852) =  2750.55712890625
               r(r851) =  2746.454833984375
               r(r850) =  2742.01220703125
               r(r849) =  2737.518798828125
               r(r848) =  2734.6826171875
               r(r847) =  2732.52197265625
               r(r846) =  2729.0908203125
               r(r845) =  2726.293212890625
               r(r844) =  2723.23388671875
               r(r843) =  2720.448974609375
               r(r842) =  2717.676513671875
               r(r841) =  2714.723876953125
               r(r840) =  2712.126220703125
               r(r839) =  2707.51904296875
               r(r838) =  2704.5048828125
               r(r837) =  2701.02099609375
               r(r836) =  2698.33642578125
               r(r835) =  2695.824462890625
               r(r834) =  2693.14599609375
               r(r833) =  2690.723388671875
               r(r832) =  2688.46533203125
               r(r831) =  2684.8828125
               r(r830) =  2682.240478515625
               r(r829) =  2679.878173828125
               r(r828) =  2676.61962890625
               r(r827) =  2673.962646484375
               r(r826) =  2671.537841796875
               r(r825) =  2669.1357421875
               r(r824) =  2666.544189453125
               r(r823) =  2663.75341796875
               r(r822) =  2660.81982421875
               r(r821) =  2659.294921875
               r(r820) =  2657.724365234375
               r(r819) =  2655.095703125
               r(r818) =  2654.050537109375
               r(r817) =  2651.55859375
               r(r816) =  2648.925048828125
               r(r815) =  2647.1044921875
               r(r814) =  2644.0302734375
               r(r813) =  2641.451904296875
               r(r812) =  2639.403076171875
               r(r811) =  2636.806884765625
               r(r810) =  2634.904296875
               r(r809) =  2632.105224609375
               r(r808) =  2629.72021484375
               r(r807) =  2626.77978515625
               r(r806) =  2624.459716796875
               r(r805) =  2622.302001953125
               r(r804) =  2619.643798828125
               r(r803) =  2617.572509765625
               r(r802) =  2614.391357421875
               r(r801) =  2611.363525390625
               r(r800) =  2609.2294921875
               r(r799) =  2606.28076171875
               r(r798) =  2603.378662109375
               r(r797) =  2600.864501953125
               r(r796) =  2598.93212890625
               r(r795) =  2596.141357421875
               r(r794) =  2594.1982421875
               r(r793) =  2590.798095703125
               r(r792) =  2588.099609375
               r(r791) =  2585.393798828125
               r(r790) =  2583.8466796875
               r(r789) =  2581.680419921875
               r(r788) =  2580.084716796875
               r(r787) =  2577.237548828125
               r(r786) =  2575.64892578125
               r(r785) =  2573.090087890625
               r(r784) =  2570.572509765625
               r(r783) =  2569.959716796875
               r(r782) =  2568.3525390625
               r(r781) =  2565.9111328125
               r(r780) =  2564.039306640625
               r(r779) =  2561.889892578125
               r(r778) =  2560.072265625
               r(r777) =  2557.543701171875
               r(r776) =  2555.7392578125
               r(r775) =  2553.409423828125
               r(r774) =  2551.79443359375
               r(r773) =  2549.97705078125
               r(r772) =  2548.09814453125
               r(r771) =  2545.745849609375
               r(r770) =  2544.105712890625
               r(r769) =  2541.899169921875
               r(r768) =  2539.9443359375
               r(r767) =  2537.3466796875
               r(r766) =  2535.491943359375
               r(r765) =  2532.99462890625
               r(r764) =  2530.388916015625
               r(r763) =  2527.76416015625
               r(r762) =  2526.046875
               r(r761) =  2524.5556640625
               r(r760) =  2522.3125
               r(r759) =  2520.017822265625
               r(r758) =  2517.888427734375
               r(r757) =  2516.20654296875
               r(r756) =  2514.437255859375
               r(r755) =  2512.09423828125
               r(r754) =  2509.71240234375
               r(r753) =  2507.7734375
               r(r752) =  2505.9560546875
               r(r751) =  2504.09326171875
               r(r750) =  2502.523193359375
               r(r749) =  2500.310791015625
               r(r748) =  2498.497314453125
               r(r747) =  2496.73779296875
               r(r746) =  2494.451904296875
               r(r745) =  2492.39990234375
               r(r744) =  2490.625244140625
               r(r743) =  2489.240478515625
               r(r742) =  2487.337646484375
               r(r741) =  2485.824462890625
               r(r740) =  2483.8408203125
               r(r739) =  2482.309326171875
               r(r738) =  2480.376953125
               r(r737) =  2478.090087890625
               r(r736) =  2476.130126953125
               r(r735) =  2474.454833984375
               r(r734) =  2472.65966796875
               r(r733) =  2470.789306640625
               r(r732) =  2467.9765625
               r(r731) =  2466.37841796875
               r(r730) =  2464.76220703125
               r(r729) =  2462.944580078125
               r(r728) =  2461.0576171875
               r(r727) =  2458.947265625
               r(r726) =  2457.694580078125
               r(r725) =  2455.46142578125
               r(r724) =  2453.494140625
               r(r723) =  2451.6484375
               r(r722) =  2449.284423828125
               r(r721) =  2447.515380859375
               r(r720) =  2445.376708984375
               r(r719) =  2444.07763671875
               r(r718) =  2441.339111328125
               r(r717) =  2439.83935546875
               r(r716) =  2437.68212890625
               r(r715) =  2435.700927734375
               r(r714) =  2433.4638671875
               r(r713) =  2431.251953125
               r(r712) =  2428.01123046875
               r(r711) =  2426.454833984375
               r(r710) =  2425.4501953125
               r(r709) =  2423.771240234375
               r(r708) =  2421.928466796875
               r(r707) =  2420.5400390625
               r(r706) =  2418.9248046875
               r(r705) =  2417.308349609375
               r(r704) =  2415.648681640625
               r(r703) =  2414.27978515625
               r(r702) =  2412.957763671875
               r(r701) =  2411.654541015625
               r(r700) =  2409.383544921875
               r(r699) =  2408.01953125
               r(r698) =  2406.682373046875
               r(r697) =  2405.193115234375
               r(r696) =  2403.474853515625
               r(r695) =  2402.164306640625
               r(r694) =  2400.750732421875
               r(r693) =  2399.489990234375
               r(r692) =  2397.90625
               r(r691) =  2395.57958984375
               r(r690) =  2393.885498046875
               r(r689) =  2392.26904296875
               r(r688) =  2390.684814453125
               r(r687) =  2389.442626953125
               r(r686) =  2387.2216796875
               r(r685) =  2385.6064453125
               r(r684) =  2383.385009765625
               r(r683) =  2381.5673828125
               r(r682) =  2379.807373046875
               r(r681) =  2378.538818359375
               r(r680) =  2376.65771484375
               r(r679) =  2374.68994140625
               r(r678) =  2373.288818359375
               r(r677) =  2372.075927734375
               r(r676) =  2370.95068359375
               r(r675) =  2369.364501953125
               r(r674) =  2367.99853515625
               r(r673) =  2366.043701171875
               r(r672) =  2364.135009765625
               r(r671) =  2361.79052734375
               r(r670) =  2359.88720703125
               r(r669) =  2358.02978515625
               r(r668) =  2356.7294921875
               r(r667) =  2355.401123046875
               r(r666) =  2354.100830078125
               r(r665) =  2353.0693359375
               r(r664) =  2351.81689453125
               r(r663) =  2349.8642578125
               r(r662) =  2347.64306640625
               r(r661) =  2345.888671875
               r(r660) =  2344.614013671875
               r(r659) =  2343.06103515625
               r(r658) =  2341.229736328125
               r(r657) =  2339.26171875
               r(r656) =  2337.343994140625
               r(r655) =  2335.72900390625
               r(r654) =  2333.98974609375
               r(r653) =  2332.175537109375
               r(r652) =  2330.68115234375
               r(r651) =  2328.943115234375
               r(r650) =  2328.05615234375
               r(r649) =  2326.439697265625
               r(r648) =  2324.82470703125
               r(r647) =  2323.334228515625
               r(r646) =  2321.94091796875
               r(r645) =  2320.584716796875
               r(r644) =  2319.57470703125
               r(r643) =  2318.1611328125
               r(r642) =  2316.7470703125
               r(r641) =  2314.929443359375
               r(r640) =  2313.51708984375
               r(r639) =  2311.4970703125
               r(r638) =  2309.882080078125
               r(r637) =  2308.670654296875
               r(r636) =  2306.85302734375
               r(r635) =  2306.045654296875
               r(r634) =  2304.025634765625
               r(r633) =  2302.2080078125
               r(r632) =  2300.29052734375
               r(r631) =  2299.352294921875
               r(r630) =  2298.119873046875
               r(r629) =  2296.508056640625
               r(r628) =  2295.28076171875
               r(r627) =  2293.525146484375
               r(r626) =  2291.234619140625
               r(r625) =  2289.486328125
               r(r624) =  2287.871337890625
               r(r623) =  2286.587646484375
               r(r622) =  2284.64013671875
               r(r621) =  2283.2275390625
               r(r620) =  2281.592529296875
               r(r619) =  2279.793701171875
               r(r618) =  2278.154541015625
               r(r617) =  2277.16845703125
               r(r616) =  2276.14404296875
               r(r615) =  2273.9384765625
               r(r614) =  2272.323486328125
               r(r613) =  2271.11083984375
               r(r612) =  2269.697021484375
               r(r611) =  2268.41357421875
               r(r610) =  2267.396240234375
               r(r609) =  2266.281005859375
               r(r608) =  2265.07568359375
               r(r607) =  2263.235595703125
               r(r606) =  2262.0244140625
               r(r605) =  2259.80322265625
               r(r604) =  2257.985595703125
               r(r603) =  2256.57177734375
               r(r602) =  2254.949462890625
               r(r601) =  2253.0927734375
               r(r600) =  2251.15185546875
               r(r599) =  2249.2529296875
               r(r598) =  2246.99853515625
               r(r597) =  2245.323974609375
               r(r596) =  2243.64892578125
               r(r595) =  2241.62890625
               r(r594) =  2240.6201171875
               r(r593) =  2239.13232421875
               r(r592) =  2237.59130859375
               r(r591) =  2236.715576171875
               r(r590) =  2235.540771484375
               r(r589) =  2234.697265625
               r(r588) =  2233.55224609375
               r(r587) =  2231.7431640625
               r(r586) =  2230.267333984375
               r(r585) =  2228.30224609375
               r(r584) =  2227.02001953125
               r(r583) =  2225.666259765625
               r(r582) =  2223.732666015625
               r(r581) =  2222.6484375
               r(r580) =  2221.4111328125
               r(r579) =  2219.214599609375
               r(r578) =  2217.609130859375
               r(r577) =  2215.781982421875
               r(r576) =  2214.3681640625
               r(r575) =  2213.359619140625
               r(r574) =  2211.7431640625
               r(r573) =  2210.1279296875
               r(r572) =  2209.17138671875
               r(r571) =  2207.705322265625
               r(r570) =  2205.74658203125
               r(r569) =  2204.392333984375
               r(r568) =  2202.4677734375
               r(r567) =  2201.041748046875
               r(r566) =  2199.045166015625
               r(r565) =  2197.609130859375
               r(r564) =  2195.810791015625
               r(r563) =  2192.716796875
               r(r562) =  2190.742431640625
               r(r561) =  2189.28076171875
               r(r560) =  2187.108642578125
               r(r559) =  2184.75439453125
               r(r558) =  2183.06982421875
               r(r557) =  2181.1201171875
               r(r556) =  2179.003173828125
               r(r555) =  2177.6171875
               r(r554) =  2176.607177734375
               r(r553) =  2174.386962890625
               r(r552) =  2173.174560546875
               r(r551) =  2171.62060546875
               r(r550) =  2169.47119140625
               r(r549) =  2167.84814453125
               r(r548) =  2165.90576171875
               r(r547) =  2163.603759765625
               r(r546) =  2161.866943359375
               r(r545) =  2160.04931640625
               r(r544) =  2158.635498046875
               r(r543) =  2157.629638671875
               r(r542) =  2156.5166015625
               r(r541) =  2155.190673828125
               r(r540) =  2153.459228515625
               r(r539) =  2151.770263671875
               r(r538) =  2149.326171875
               r(r537) =  2147.145263671875
               r(r536) =  2146.116455078125
               r(r535) =  2144.26025390625
               r(r534) =  2142.68359375
               r(r533) =  2140.663818359375
               r(r532) =  2138.921630859375
               r(r531) =  2137.66796875
               r(r530) =  2136.6259765625
               r(r529) =  2135.185791015625
               r(r528) =  2133.99365234375
               r(r527) =  2132.38134765625
               r(r526) =  2130.5673828125
               r(r525) =  2128.548583984375
               r(r524) =  2126.99951171875
               r(r523) =  2124.997802734375
               r(r522) =  2123.942626953125
               r(r521) =  2122.950927734375
               r(r520) =  2121.076904296875
               r(r519) =  2119.489013671875
               r(r518) =  2117.845703125
               r(r517) =  2116.43310546875
               r(r516) =  2114.711181640625
               r(r515) =  2112.35205078125
               r(r514) =  2110.374267578125
               r(r513) =  2107.699462890625
               r(r512) =  2105.631103515625
               r(r511) =  2104.11328125
               r(r510) =  2102.2978515625
               r(r509) =  2100.076416015625
               r(r508) =  2097.71533203125
               r(r507) =  2096.44140625
               r(r506) =  2094.619384765625
               r(r505) =  2092.60498046875
               r(r504) =  2090.78759765625
               r(r503) =  2090.5849609375
               r(r502) =  2089.018798828125
               r(r501) =  2087.959716796875
               r(r500) =  2086.34228515625
               r(r499) =  2084.8115234375
               r(r498) =  2082.709716796875
               r(r497) =  2080.6337890625
               r(r496) =  2079.00830078125
               r(r495) =  2076.303466796875
               r(r494) =  2074.63330078125
               r(r493) =  2073.219482421875
               r(r492) =  2071.8056640625
               r(r491) =  2069.786865234375
               r(r490) =  2068.373046875
               r(r489) =  2066.4970703125
               r(r488) =  2064.690673828125
               r(r487) =  2062.1318359375
               r(r486) =  2060.23291015625
               r(r485) =  2058.545654296875
               r(r484) =  2056.8583984375
               r(r483) =  2055.086181640625
               r(r482) =  2052.7646484375
               r(r481) =  2051.37353515625
               r(r480) =  2049.052734375
               r(r479) =  2046.4033203125
               r(r478) =  2043.98974609375
               r(r477) =  2042.5126953125
               r(r476) =  2040.9111328125
               r(r475) =  2039.698608398438
               r(r474) =  2037.871459960938
               r(r473) =  2035.5869140625
               r(r472) =  2034.495727539063
               r(r471) =  2032.375610351563
               r(r470) =  2030.446044921875
               r(r469) =  2028.5888671875
               r(r468) =  2026.268310546875
               r(r467) =  2024.55126953125
               r(r466) =  2022.76708984375
               r(r465) =  2021.416870117188
               r(r464) =  2019.729614257813
               r(r463) =  2017.566040039063
               r(r462) =  2015.407104492188
               r(r461) =  2014.255493164063
               r(r460) =  2012.236694335938
               r(r459) =  2011.025390625
               r(r458) =  2009.181518554688
               r(r457) =  2007.612182617188
               r(r456) =  2006.227661132813
               r(r455) =  2004.681762695313
               r(r454) =  2003.351318359375
               r(r453) =  2002.634399414063
               r(r452) =  2000.9541015625
               r(r451) =  1999.111328125
               r(r450) =  1997.494873046875
               r(r449) =  1995.679565429688
               r(r448) =  1994.202514648438
               r(r447) =  1993.0400390625
               r(r446) =  1991.038208007813
               r(r445) =  1990.102661132813
               r(r444) =  1988.92919921875
               r(r443) =  1987.745849609375
               r(r442) =  1986.386108398438
               r(r441) =  1984.076416015625
               r(r440) =  1981.967163085938
               r(r439) =  1980.701782226563
               r(r438) =  1979.435180664063
               r(r437) =  1978.381225585938
               r(r436) =  1976.494506835938
               r(r435) =  1975.171752929688
               r(r434) =  1973.386596679688
               r(r433) =  1972.433471679688
               r(r432) =  1970.884399414063
               r(r431) =  1969.310180664063
               r(r430) =  1967.621704101563
               r(r429) =  1966.144653320313
               r(r428) =  1964.58056640625
               r(r427) =  1962.758666992188
               r(r426) =  1961.504638671875
               r(r425) =  1960.23779296875
               r(r424) =  1958.55078125
               r(r423) =  1957.49560546875
               r(r422) =  1956.019653320313
               r(r421) =  1954.542846679688
               r(r420) =  1952.643920898438
               r(r419) =  1951.03564453125
               r(r418) =  1949.690063476563
               r(r417) =  1947.416259765625
               r(r416) =  1945.80126953125
               r(r415) =  1944.205078125
               r(r414) =  1942.776977539063
               r(r413) =  1941.444946289063
               r(r412) =  1939.775268554688
               r(r411) =  1938.808349609375
               r(r410) =  1937.989379882813
               r(r409) =  1937.032958984375
               r(r408) =  1935.957885742188
               r(r407) =  1935.099609375
               r(r406) =  1934.078979492188
               r(r405) =  1932.67578125
               r(r404) =  1931.758178710938
               r(r403) =  1930.492797851563
               r(r402) =  1929.437622070313
               r(r401) =  1928.636962890625
               r(r400) =  1928.172241210938
               r(r399) =  1927.177978515625
               r(r398) =  1926.063110351563
               r(r397) =  1924.974853515625
               r(r396) =  1923.530883789063
               r(r395) =  1921.638671875
               r(r394) =  1920.560424804688
               r(r393) =  1919.312744140625
               r(r392) =  1917.835693359375
               r(r391) =  1916.727416992188
               r(r390) =  1915.719848632813
               r(r389) =  1915.005004882813
               r(r388) =  1913.694091796875
               r(r387) =  1912.561157226563
               r(r386) =  1911.295654296875
               r(r385) =  1909.818603515625
               r(r384) =  1908.872924804688
               r(r383) =  1908.079223632813
               r(r382) =  1906.277954101563
               r(r381) =  1905.1640625
               r(r380) =  1904.12353515625
               r(r379) =  1902.856689453125
               r(r378) =  1901.755737304688
               r(r377) =  1900.738159179688
               r(r376) =  1899.168823242188
               r(r375) =  1897.793701171875
               r(r374) =  1896.73974609375
               r(r373) =  1895.05126953125
               r(r372) =  1893.997314453125
               r(r371) =  1892.467407226563
               r(r370) =  1890.50146484375
               r(r369) =  1889.7373046875
               r(r368) =  1888.5673828125
               r(r367) =  1887.245727539063
               r(r366) =  1886.232055664063
               r(r365) =  1885.34814453125
               r(r364) =  1884.29296875
               r(r363) =  1883.027465820313
               r(r362) =  1881.972290039063
               r(r361) =  1880.495361328125
               r(r360) =  1878.760620117188
               r(r359) =  1877.542602539063
               r(r358) =  1876.065551757813
               r(r357) =  1875.010375976563
               r(r356) =  1873.577392578125
               r(r355) =  1872.26806640625
               r(r354) =  1871.437377929688
               r(r353) =  1869.947265625
               r(r352) =  1868.925659179688
               r(r351) =  1867.873168945313
               r(r350) =  1866.99462890625
               r(r349) =  1865.517700195313
               r(r348) =  1864.462524414063
               r(r347) =  1862.742065429688
               r(r346) =  1861.820190429688
               r(r345) =  1860.876342773438
               r(r344) =  1860.1826171875
               r(r343) =  1858.962158203125
               r(r342) =  1857.712158203125
               r(r341) =  1856.446655273438
               r(r340) =  1855.179931640625
               r(r339) =  1854.336303710938
               r(r338) =  1853.036010742188
               r(r337) =  1852.13525390625
               r(r336) =  1851.383544921875
               r(r335) =  1850.328369140625
               r(r334) =  1849.37158203125
               r(r333) =  1848.18359375
               r(r332) =  1847.390258789063
               r(r331) =  1846.599365234375
               r(r330) =  1845.475219726563
               r(r329) =  1844.246826171875
               r(r328) =  1843.075073242188
               r(r327) =  1841.67919921875
               r(r326) =  1840.413818359375
               r(r325) =  1839.56884765625
               r(r324) =  1838.725219726563
               r(r323) =  1837.653076171875
               r(r322) =  1836.261962890625
               r(r321) =  1834.900268554688
               r(r320) =  1833.873657226563
               r(r319) =  1832.390747070313
               r(r318) =  1831.306884765625
               r(r317) =  1830.135620117188
               r(r316) =  1829.150634765625
               r(r315) =  1827.75537109375
               r(r314) =  1826.857421875
               r(r313) =  1825.93359375
               r(r312) =  1825.224487304688
               r(r311) =  1824.473022460938
               r(r310) =  1823.114135742188
               r(r309) =  1821.8486328125
               r(r308) =  1821.133911132813
               r(r307) =  1820.0712890625
               r(r306) =  1819.09521484375
               r(r305) =  1818.262573242188
               r(r304) =  1817.406616210938
               r(r303) =  1816.695190429688
               r(r302) =  1815.910034179688
               r(r301) =  1814.748901367188
               r(r300) =  1813.432495117188
               r(r299) =  1812.224731445313
               r(r298) =  1811.089111328125
               r(r297) =  1809.877197265625
               r(r296) =  1808.769775390625
               r(r295) =  1807.882446289063
               r(r294) =  1806.5087890625
               r(r293) =  1805.597778320313
               r(r292) =  1804.53466796875
               r(r291) =  1803.598754882813
               r(r290) =  1802.054443359375
               r(r289) =  1800.964233398438
               r(r288) =  1799.4873046875
               r(r287) =  1798.571044921875
               r(r286) =  1796.964965820313
               r(r285) =  1795.901245117188
               r(r284) =  1794.790283203125
               r(r283) =  1793.922607421875
               r(r282) =  1793.087524414063
               r(r281) =  1792.287353515625
               r(r280) =  1791.164916992188
               r(r279) =  1790.78955078125
               r(r278) =  1789.65283203125
               r(r277) =  1788.317016601563
               r(r276) =  1786.882080078125
               r(r275) =  1785.985229492188
               r(r274) =  1785.064331054688
               r(r273) =  1784.310791015625
               r(r272) =  1783.454345703125
               r(r271) =  1782.61083984375
               r(r270) =  1781.519165039063
               r(r269) =  1780.303344726563
               r(r268) =  1779.23486328125
               r(r267) =  1777.969482421875
               r(r266) =  1777.256713867188
               r(r265) =  1776.280883789063
               r(r264) =  1775.534057617188
               r(r263) =  1775.003295898438
               r(r262) =  1774.382080078125
               r(r261) =  1773.11669921875
               r(r260) =  1772.290649414063
               r(r259) =  1771.639770507813
               r(r258) =  1770.796020507813
               r(r257) =  1769.530639648438
               r(r256) =  1768.506469726563
               r(r255) =  1767.2099609375
               r(r254) =  1765.950073242188
               r(r253) =  1764.46826171875
               r(r252) =  1762.990478515625
               r(r251) =  1762.3583984375
               r(r250) =  1761.223876953125
               r(r249) =  1760.248046875
               r(r248) =  1759.125
               r(r247) =  1758.166015625
               r(r246) =  1757.083740234375
               r(r245) =  1755.8818359375
               r(r244) =  1754.76318359375
               r(r243) =  1753.739868164063
               r(r242) =  1752.864379882813
               r(r241) =  1751.875366210938
               r(r240) =  1750.285400390625
               r(r239) =  1749.278198242188
               r(r238) =  1748.528198242188
               r(r237) =  1747.677856445313
               r(r236) =  1746.535766601563
               r(r235) =  1745.270385742188
               r(r234) =  1744.243286132813
               r(r233) =  1743.338745117188
               r(r232) =  1742.137451171875
               r(r231) =  1741.050903320313
               r(r230) =  1739.995727539063
               r(r229) =  1739.152099609375
               r(r228) =  1738.901000976563
               r(r227) =  1737.498046875
               r(r226) =  1736.71044921875
               r(r225) =  1735.468994140625
               r(r224) =  1733.878784179688
               r(r223) =  1732.22314453125
               r(r222) =  1731.55810546875
               r(r221) =  1729.974487304688
               r(r220) =  1728.598388671875
               r(r219) =  1727.398559570313
               r(r218) =  1726.60595703125
               r(r217) =  1725.545288085938
               r(r216) =  1724.570922851563
               r(r215) =  1723.06494140625
               r(r214) =  1721.538940429688
               r(r213) =  1720.2568359375
               r(r212) =  1719.3544921875
               r(r211) =  1718.104370117188
               r(r210) =  1717.002197265625
               r(r209) =  1715.947021484375
               r(r208) =  1714.7216796875
               r(r207) =  1713.716552734375
               r(r206) =  1712.47216796875
               r(r205) =  1711.830200195313
               r(r204) =  1710.672485351563
               r(r203) =  1709.46142578125
               r(r202) =  1708.326293945313
               r(r201) =  1707.35009765625
               r(r200) =  1706.657104492188
               r(r199) =  1705.399047851563
               r(r198) =  1704.749755859375
               r(r197) =  1703.711669921875
               r(r196) =  1702.840942382813
               r(r195) =  1701.505126953125
               r(r194) =  1701.123779296875
               r(r193) =  1700.000244140625
               r(r192) =  1698.536987304688
               r(r191) =  1698.015380859375
               r(r190) =  1697.311889648438
               r(r189) =  1696.478637695313
               r(r188) =  1695.730346679688
               r(r187) =  1694.60791015625
               r(r186) =  1693.49267578125
               r(r185) =  1691.801025390625
               r(r184) =  1690.631591796875
               r(r183) =  1689.676513671875
               r(r182) =  1688.806640625
               r(r181) =  1688.058349609375
               r(r180) =  1687.386352539063
               r(r179) =  1686.253295898438
               r(r178) =  1685.6884765625
               r(r177) =  1684.33447265625
               r(r176) =  1683.669677734375
               r(r175) =  1682.484252929688
               r(r174) =  1681.134643554688
               r(r173) =  1679.755249023438
               r(r172) =  1678.532592773438
               r(r171) =  1677.27490234375
               r(r170) =  1676.455932617188
               r(r169) =  1676.129272460938
               r(r168) =  1676.129272460938
               r(r167) =  1674.471557617188
               r(r166) =  1672.489624023438
               r(r165) =  1670.966064453125
               r(r164) =  1669.747192382813
               r(r163) =  1668.270141601563
               r(r162) =  1666.733764648438
               r(r161) =  1664.93798828125
               r(r160) =  1662.774658203125
               r(r159) =  1660.674926757813
               r(r158) =  1658.354248046875
               r(r157) =  1656.838134765625
               r(r156) =  1655.02099609375
               r(r155) =  1653.440063476563
               r(r154) =  1651.326293945313
               r(r153) =  1650.084716796875
               r(r152) =  1648.274291992188
               r(r151) =  1646.752319335938
               r(r150) =  1645.412475585938
               r(r149) =  1644.457885742188
               r(r148) =  1643.62353515625
               r(r147) =  1642.497680664063
               r(r146) =  1640.423828125
               r(r145) =  1639.116088867188
               r(r144) =  1637.5888671875
               r(r143) =  1637.20751953125
               r(r142) =  1635.597412109375
               r(r141) =  1633.856567382813
               r(r140) =  1633.277099609375
               r(r139) =  1633.277099609375
               r(r138) =  1630.966186523438
               r(r137) =  1628.240600585938
               r(r136) =  1626.3330078125
               r(r135) =  1624.389770507813
               r(r134) =  1622.325561523438
               r(r133) =  1620.895141601563
               r(r132) =  1619.702758789063
               r(r131) =  1617.639282226563
               r(r130) =  1614.896850585938
               r(r129) =  1613.083129882813
               r(r128) =  1609.839599609375
               r(r127) =  1608.22314453125
               r(r126) =  1606.108032226563
               r(r125) =  1604.581909179688
               r(r124) =  1603.579956054688
               r(r123) =  1602.864624023438
               r(r122) =  1601.171997070313
               r(r121) =  1598.884521484375
               r(r120) =  1597.884399414063
               r(r119) =  1596.568237304688
               r(r118) =  1594.596801757813
               r(r117) =  1592.943481445313
               r(r116) =  1590.425170898438
               r(r115) =  1588.316040039063
               r(r114) =  1586.456787109375
               r(r113) =  1585.034057617188
               r(r112) =  1583.703857421875
               r(r111) =  1582.449462890625
               r(r110) =  1580.5419921875
               r(r109) =  1579.206176757813
               r(r108) =  1578.061645507813
               r(r107) =  1577.993896484375
               r(r106) =  1577.680053710938
               r(r105) =  1577.680053710938
               r(r104) =  1576.502563476563
               r(r103) =  1575.034790039063
               r(r102) =  1573.33837890625
               r(r101) =  1571.895751953125
               r(r100) =  1569.486328125
                r(r99) =  1567.906860351563
                r(r98) =  1564.482788085938
                r(r97) =  1563.432495117188
                r(r96) =  1562.941040039063
                r(r95) =  1561.523681640625
                r(r94) =  1559.626098632813
                r(r93) =  1557.9375
                r(r92) =  1556.134643554688
                r(r91) =  1554.78466796875
                r(r90) =  1553.5078125
                r(r89) =  1549.984497070313
                r(r88) =  1548.444702148438
                r(r87) =  1546.757446289063
                r(r86) =  1544.907470703125
                r(r85) =  1542.383056640625
                r(r84) =  1540.005737304688
                r(r83) =  1538.509887695313
                r(r82) =  1537.736450195313
                r(r81) =  1537.736450195313
                r(r80) =  1535.802856445313
                r(r79) =  1532.949584960938
                r(r78) =  1530.55322265625
                r(r77) =  1527.69140625
                r(r76) =  1525.239624023438
                r(r75) =  1523.530639648438
                r(r74) =  1520.060302734375
                r(r73) =  1518.59130859375
                r(r72) =  1516.052856445313
                r(r71) =  1514.335571289063
                r(r70) =  1511.315795898438
                r(r69) =  1508.230834960938
                r(r68) =  1505.36865234375
                r(r67) =  1503.510375976563
                r(r66) =  1499.453735351563
                r(r65) =  1495.916381835938
                r(r64) =  1492.750854492188
                r(r63) =  1489.330688476563
                r(r62) =  1485.526123046875
                r(r61) =  1483.530639648438
                r(r60) =  1479.802612304688
                r(r59) =  1477.130981445313
                r(r58) =  1472.001586914063
                r(r57) =  1468.068725585938
                r(r56) =  1464.17822265625
                r(r55) =  1460.150390625
                r(r54) =  1459.005859375
                r(r53) =  1458.576049804688
                r(r52) =  1453.663696289063
                r(r51) =  1448.238159179688
                r(r50) =  1442.554565429688
                r(r49) =  1438.908203125
                r(r48) =  1431.783569335938
                r(r47) =  1425.756469726563
                r(r46) =  1422.855590820313
                r(r45) =  1422.855590820313
                r(r44) =  1417.440551757813
                r(r43) =  1406.755126953125
                r(r42) =  1401.194702148438
                r(r41) =  1395.076538085938
                r(r40) =  1386.453857421875
                r(r39) =  1374.539794921875
                r(r38) =  1363.0107421875
                r(r37) =  1351.045166015625
                r(r36) =  1339.43896484375
                r(r35) =  1336.204467773438
                r(r34) =  1327.582763671875
                r(r33) =  1312.229858398438
                r(r32) =  1297.613647460938
                r(r31) =  1274.440063476563
                r(r30) =  1258.41845703125
                r(r29) =  1236.646484375
                r(r28) =  1220.402587890625
                r(r27) =  1189.185180664063
                r(r26) =  1167.451782226563
                r(r25) =  1127.396118164063
                r(r24) =  1088.763793945313
                r(r23) =  1054.480834960938
                r(r22) =  1018.321960449219
                r(r21) =  981.3638916015625
r; t=0.13 15:14:24

. 
.         * full sample: contrast pre-vs-post-imputation coal & non-coal wages
.         sum wctentend_preimp wcoal_end_monthly, d

         daily last wage in coal pre-top-code-impute
-------------------------------------------------------------
      Percentiles      Smallest
 1%     25.56212       10.52435
 5%     47.39491       10.92557
10%     51.55606         11.527       Obs              22,271
25%     57.85632       12.10428       Sum of wgt.      22,271

50%     68.56995                      Mean           72.56898
                        Largest       Std. dev.      34.36648
75%     82.24436       1066.937
90%     97.25443       1066.937       Variance       1181.055
95%     111.0522       1066.937       Skewness       35.77703
99%       146.36       3047.483       Kurtosis       2684.863

                    monthly wage in coal
-------------------------------------------------------------
      Percentiles      Smallest
 1%     778.3665       320.4664
 5%     1443.175       332.6837
10%     1569.882       350.9971       Obs              22,271
25%     1761.725       368.5754       Sum of wgt.      22,271

50%     2087.955                      Mean           2211.388
                        Largest       Std. dev.      814.2942
75%     2504.341       18082.79
90%     2961.397        20594.5       Variance       663075.1
95%      3389.98          21968       Skewness       6.986502
99%     4622.802       33257.08       Kurtosis       159.5695
r; t=0.53 15:14:24

.         
.         * by cells - distribution analysis 
.         forvalues i = 1/$cellnumber {
  2.                 local j = `i'+2
  3.                 di "Distribution of wage in lignite before transition to unemployment for cell `i'"     
  4.                         *by cells - normal distribution paramaters
.                         capture noisily estpost sum wcoal_end_monthly if cell == `i', d 
  5.                         if _rc!=2000{
  6.                                 putexcel set results/${samplefolder}/5_sample${sample}_wages_distribution, sheet("wcoal_normal") modify
  7.                                 #delimit ; 
delimiter now ;
.                                 putexcel A`j'=("wcoal_end_monthly_`i'") B`j'=matrix(e(mean)) C`j'=matrix(e(sd)) D`j'=matrix(e(sum_w)) E`j'=matrix(e(skewness)) 
>                                 F`j'=matrix(e(kurtosis)) G`j'=matrix(e(sum)) H`j'=matrix(e(min)) I`j'=matrix(e(max)) J`j'=matrix(e(p1)) K`j'=matrix(e(p5)) 
>                                 L`j'=matrix(e(p10)) M`j'=matrix(e(p25)) N`j'=matrix(e(p50)) O`j'=matrix(e(p75)) P`j'=matrix(e(p90)) Q`j'=matrix(e(p95)) R`j'=matrix(e(p99));
  8.                                 #delimit cr
delimiter now cr
.                         `       putexcelclose'
  9.                         }
 10.                         *by cells - normal distribution fit
.                         capture noisily dpplot wcoal_end_monthly  if cell == `i' & wcoal_end_monthly<=$incmax, caption("")
 11.                         if _rc!=2000 & _rc!=2001 {
 12.                                 graph export results/${samplefolder}/5_sample${sample}_wcoal_dpplot_`i'.pdf, replace
 13.                         }
 14.                         capture noisily kdensity wcoal_end_monthly if cell == `i', normal
 15.                         if _rc!=2000 & _rc!=2001 & _rc!=198{
 16.                                 graph export results/${samplefolder}/5_sample${sample}_wcoal_kdensity_`i'.pdf, replace
 17.                         }
 18.                         capture noisily qnorm wcoal_end_monthly if cell == `i'
 19.                         if _rc!=2000 & _rc!=2001{
 20.                         graph export results/${samplefolder}/5_sample${sample}_wcoal_qnorm_`i'.pdf, replace
 21.                         }
 22.                         capture noisily sktest wcoal_end_monthly if cell == `i'
 23.                         
.                         *by cells - lognormal distribution parameters
.                         capture noisily lognfit wcoal_end_monthly if cell == `i', stats
 24.                         if _rc!=2000 & _rc!=2001{       
 25.                                 putexcel set results/${samplefolder}/5_sample${sample}_wages_distribution, sheet("wcoal_lognormal") modify
 26.                                 putexcel A`j'=("wcoal_end_monthly_`i'") B`j'=(e(bm)) C`j'=(e(bv)) 
 27.                                 `putexcelclose'
 28.                         }
 29.                         
.                         *by cells - lognormal distribution fit
.                         capture noisily dpplot wcoal_end_monthly if cell == `i' & wcoal_end_monthly<=$incmax, dist(lognormal) param(`e(bm)' `e(bv)') caption("") 
 30.                         if _rc!=2000 & _rc!=2001{
 31.                                 graph export results/${samplefolder}/5_sample${sample}_wcoal_dpplot_logn_`i'.pdf, replace
 32.                         }
 33.         }
Distribution of wage in lignite before transition to unemployment for cell 1

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wcoal_end_~y |       644        644   1762.306   753867.3   868.2553   1.036534    6.09541    1134925   432.4261   7051.937    550.262   600.2899   777.4163   942.7016   1790.175   2274.443 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wcoal_end_~y |  2734.313   3223.916    4216.81 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_1.pdf saved as PDF format
file results/two/5_sample2_wcoal_kdensity_1.pdf saved as PDF format
file results/two/5_sample2_wcoal_qnorm_1.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                              ----- Joint test -----
         Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
------------------+-----------------------------------------------------------------
wcoal_end_monthly |       644         0.0000         0.0000        104.01     0.0000

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood =  -65607.19
rescale:       log likelihood = -6446.7275
rescale eq:    log likelihood = -5548.9354
Iteration 0:   log likelihood = -5548.9354  (not concave)
Iteration 1:   log likelihood =  -5318.974  
Iteration 2:   log likelihood = -5233.4788  
Iteration 3:   log likelihood = -5228.9986  
Iteration 4:   log likelihood = -5228.9647  
Iteration 5:   log likelihood = -5228.9647  

ML fit of lognormal distribution                  Number of obs   =        644
                                                  Wald chi2(0)    =          .
Log likelihood = -5228.9647                       Prob > chi2     =          .

------------------------------------------------------------------------------
wcoal_end_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.346631   .0206527   355.72   0.000     7.306153     7.38711
-------------+----------------------------------------------------------------
v            |
       _cons |   .5241071   .0146037    35.89   0.000     .4954844    .5527297
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wcoal_end_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  458.23216       0.00218
 5%  654.94542       0.01504
10%  792.31661       0.03549
20%  997.77886       0.08601
25%   1.09e+03       0.11534
30%   1.18e+03       0.14720
40%   1.36e+03       0.21845 Mode         1.18e+03
50%   1.55e+03       0.30010 Mean         1.78e+03
60%   1.77e+03       0.39329 Std. Dev.    1.00e+03
70%   2.04e+03       0.50012
75%   2.21e+03       0.55977 Variance     1.00e+06
80%   2.41e+03       0.62457 Half CV^2     0.15806
90%   3.04e+03       0.77561 Gini coeff.   0.28906
95%   3.67e+03       0.86880 p90/p10       3.83182
99%   5.25e+03       0.96425 p75/p25       2.02792
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_logn_1.pdf saved as PDF format
Distribution of wage in lignite before transition to unemployment for cell 2

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wcoal_end_~y |       110        110   1840.849    1357373   1165.063   .2887632   1.579418   202493.4   510.1369   4721.561   527.8057   579.3022   622.2045   744.6441    1841.91   2833.252 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wcoal_end_~y |  3354.526   3608.668   3894.731 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_2.pdf saved as PDF format
file results/two/5_sample2_wcoal_kdensity_2.pdf saved as PDF format
file results/two/5_sample2_wcoal_qnorm_2.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                              ----- Joint test -----
         Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
------------------+-----------------------------------------------------------------
wcoal_end_monthly |       110         0.1977         0.0000         96.78     0.0000

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -11055.914
rescale:       log likelihood = -1093.3388
rescale eq:    log likelihood = -958.38487
Iteration 0:   log likelihood = -958.38487  
Iteration 1:   log likelihood = -953.05176  (backed up)
Iteration 2:   log likelihood = -920.21939  
Iteration 3:   log likelihood = -919.82757  
Iteration 4:   log likelihood = -919.81815  
Iteration 5:   log likelihood = -919.81815  

ML fit of lognormal distribution                  Number of obs   =        110
                                                  Wald chi2(0)    =          .
Log likelihood = -919.81815                       Prob > chi2     =          .

------------------------------------------------------------------------------
wcoal_end_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.281775   .0679509   107.16   0.000     7.148593    7.414956
-------------+----------------------------------------------------------------
v            |
       _cons |   .7126748   .0480485    14.83   0.000     .6185015    .8068482
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wcoal_end_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  276.95224       0.00119
 5%  450.12660       0.00920
10%  583.15039       0.02306
20%  797.89131       0.06006
25%  898.81999       0.08270
30%   1.00e+03       0.10803
40%   1.21e+03       0.16702 Mode        874.68974
50%   1.45e+03       0.23802 Mean         1.87e+03
60%   1.74e+03       0.32300 Std. Dev.    1.52e+03
70%   2.11e+03       0.42533
75%   2.35e+03       0.48477 Variance     2.32e+06
80%   2.65e+03       0.55130 Half CV^2     0.33090
90%   3.62e+03       0.71528 Gini coeff.   0.38569
95%   4.69e+03       0.82438 p90/p10       6.21309
99%   7.63e+03       0.94670 p75/p25       2.61531
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_logn_2.pdf saved as PDF format
Distribution of wage in lignite before transition to unemployment for cell 3

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wcoal_end_~y |       481        481   2292.244   431834.6   657.1412   1.157283   5.044238    1102569   589.6417   5663.486   1326.687   1482.664   1579.245     1826.7    2222.85   2545.746 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wcoal_end_~y |  3301.359   3571.703   4180.382 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_3.pdf saved as PDF format
file results/two/5_sample2_wcoal_kdensity_3.pdf saved as PDF format
file results/two/5_sample2_wcoal_qnorm_3.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                              ----- Joint test -----
         Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
------------------+-----------------------------------------------------------------
wcoal_end_monthly |       481         0.0000         0.0000         76.78     0.0000

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -53745.772
rescale:       log likelihood = -5019.0655
rescale eq:    log likelihood = -3970.8805
Iteration 0:   log likelihood = -3970.8805  (not concave)
Iteration 1:   log likelihood = -3810.2058  
Iteration 2:   log likelihood = -3769.6097  
Iteration 3:   log likelihood = -3762.2662  
Iteration 4:   log likelihood = -3762.2527  
Iteration 5:   log likelihood = -3762.2527  

ML fit of lognormal distribution                  Number of obs   =        481
                                                  Wald chi2(0)    =          .
Log likelihood = -3762.2527                       Prob > chi2     =          .

------------------------------------------------------------------------------
wcoal_end_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.699403   .0124686   617.51   0.000     7.674965    7.723841
-------------+----------------------------------------------------------------
v            |
       _cons |   .2734571   .0088166    31.02   0.000     .2561768    .2907373
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wcoal_end_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%   1.17e+03       0.00466
 5%   1.41e+03       0.02754
10%   1.55e+03       0.05997
20%   1.75e+03       0.13241
25%   1.84e+03       0.17158
30%   1.91e+03       0.21248
40%   2.06e+03       0.29916 Mode         2.05e+03
50%   2.21e+03       0.39225 Mean         2.29e+03
60%   2.37e+03       0.49198 Std. Dev.   638.41802
70%   2.55e+03       0.59907
75%   2.65e+03       0.65580 Variance     4.08e+05
80%   2.78e+03       0.71504 Half CV^2     0.03882
90%   3.13e+03       0.84330 Gini coeff.   0.15333
95%   3.46e+03       0.91487 p90/p10       2.01556
99%   4.17e+03       0.97996 p75/p25       1.44613
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_logn_3.pdf saved as PDF format
Distribution of wage in lignite before transition to unemployment for cell 4

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   3231.389   672575.1   820.1068   3.110291   25.52057   319907.5   350.9971   8881.237   350.9971   2472.198    2607.59   2903.359   3124.915   3481.425 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wcoal_end_~y |  3936.328   4197.218   8881.237 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_4.pdf saved as PDF format
file results/two/5_sample2_wcoal_kdensity_4.pdf saved as PDF format
file results/two/5_sample2_wcoal_qnorm_4.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                              ----- Joint test -----
         Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
------------------+-----------------------------------------------------------------
wcoal_end_monthly |        NA         0.0000         0.0000         71.76     0.0000

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -12115.541
rescale:       log likelihood = -1075.9022
rescale eq:    log likelihood = -817.87646
Iteration 0:   log likelihood = -817.87646  
Iteration 1:   log likelihood = -813.93956  
Iteration 2:   log likelihood = -813.90408  
Iteration 3:   log likelihood = -813.90403  

ML fit of lognormal distribution                  Number of obs   =         NA
                                                  Wald chi2(0)    =          .
Log likelihood = -813.90403                       Prob > chi2     =          .

------------------------------------------------------------------------------
wcoal_end_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   8.047845   .0289238   278.24   0.000     7.991156    8.104535
-------------+----------------------------------------------------------------
v            |
       _cons |   .2877879   .0204522    14.07   0.000     .2477024    .3278734
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wcoal_end_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%   1.60e+03       0.00447
 5%   1.95e+03       0.02664
10%   2.16e+03       0.05828
20%   2.45e+03       0.12936
25%   2.58e+03       0.16796
30%   2.69e+03       0.20834
40%   2.91e+03       0.29421 Mode         2.88e+03
50%   3.13e+03       0.38675 Mean         3.26e+03
60%   3.36e+03       0.48626 Std. Dev.   957.73658
70%   3.64e+03       0.59352
75%   3.80e+03       0.65051 Variance     9.17e+05
80%   3.98e+03       0.71015 Half CV^2     0.04317
90%   4.52e+03       0.83983 Gini coeff.   0.16125
95%   5.02e+03       0.91262 p90/p10       2.09097
99%   6.11e+03       0.97925 p75/p25       1.47435
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_logn_4.pdf saved as PDF format
Distribution of wage in lignite before transition to unemployment for cell 5

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wcoal_end_~y |       115        115   2282.723   483783.9   695.5458   1.173259   3.842605   262513.1   1422.856   4374.079   1456.144   1514.908   1547.917   1757.026   2140.183    2632.53 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wcoal_end_~y |  3324.786   3820.728    4289.29 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_5.pdf saved as PDF format
file results/two/5_sample2_wcoal_kdensity_5.pdf saved as PDF format
file results/two/5_sample2_wcoal_qnorm_5.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                              ----- Joint test -----
         Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
------------------+-----------------------------------------------------------------
wcoal_end_monthly |       115         0.0000         0.0748         18.47     0.0001

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -12826.702
rescale:       log likelihood = -1199.0153
rescale eq:    log likelihood =  -950.0818
Iteration 0:   log likelihood =  -950.0818  (not concave)
Iteration 1:   log likelihood = -911.91022  
Iteration 2:   log likelihood = -902.38914  
Iteration 3:   log likelihood = -900.45629  
Iteration 4:   log likelihood = -900.45409  
Iteration 5:   log likelihood = -900.45409  

ML fit of lognormal distribution                  Number of obs   =        115
                                                  Wald chi2(0)    =          .
Log likelihood = -900.45409                       Prob > chi2     =          .

------------------------------------------------------------------------------
wcoal_end_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.692499   .0258908   297.11   0.000     7.641753    7.743244
-------------+----------------------------------------------------------------
v            |
       _cons |   .2776479   .0183076    15.17   0.000     .2417658    .3135301
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wcoal_end_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%   1.15e+03       0.00461
 5%   1.39e+03       0.02727
10%   1.54e+03       0.05947
20%   1.74e+03       0.13151
25%   1.82e+03       0.17051
30%   1.89e+03       0.21126
40%   2.04e+03       0.29771 Mode         2.03e+03
50%   2.19e+03       0.39064 Mean         2.28e+03
60%   2.35e+03       0.49031 Std. Dev.   644.86253
70%   2.54e+03       0.59745
75%   2.64e+03       0.65426 Variance     4.16e+05
80%   2.77e+03       0.71361 Half CV^2     0.04007
90%   3.13e+03       0.84229 Gini coeff.   0.15565
95%   3.46e+03       0.91422 p90/p10       2.03733
99%   4.18e+03       0.97975 p75/p25       1.45432
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_logn_5.pdf saved as PDF format
Distribution of wage in lignite before transition to unemployment for cell 6

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   3127.623   218680.2   467.6326      2.781   13.54014   128232.6   2282.254   5272.298   2282.254   2654.969    2715.54   2939.487   3071.784   3207.533 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wcoal_end_~y |  3294.138    3381.54   5272.298 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_6.pdf saved as PDF format
file results/two/5_sample2_wcoal_kdensity_6.pdf saved as PDF format
file results/two/5_sample2_wcoal_qnorm_6.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                              ----- Joint test -----
         Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
------------------+-----------------------------------------------------------------
wcoal_end_monthly |        NA         0.0000         0.0000         33.47     0.0000

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood =  -5000.832
rescale:       log likelihood = -445.03729
rescale eq:    log likelihood = -305.80357
Iteration 0:   log likelihood = -305.80357  
Iteration 1:   log likelihood = -303.86387  
Iteration 2:   log likelihood = -303.76768  
Iteration 3:   log likelihood = -303.76768  

ML fit of lognormal distribution                  Number of obs   =         NA
                                                  Wald chi2(0)    =          .
Log likelihood = -303.76768                       Prob > chi2     =          .

------------------------------------------------------------------------------
wcoal_end_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   8.039015   .0201254   399.45   0.000      7.99957     8.07846
-------------+----------------------------------------------------------------
v            |
       _cons |   .1288656   .0142308     9.06   0.000     .1009737    .1567574
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wcoal_end_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%   2.30e+03       0.00704
 5%   2.51e+03       0.03805
10%   2.63e+03       0.07921
20%   2.78e+03       0.16590
25%   2.84e+03       0.21088
30%   2.90e+03       0.25679
40%   3.00e+03       0.35115 Mode         3.05e+03
50%   3.10e+03       0.44873 Mean         3.13e+03
60%   3.20e+03       0.54953 Std. Dev.   404.43451
70%   3.32e+03       0.65378
75%   3.38e+03       0.70734 Variance     1.64e+05
80%   3.45e+03       0.76200 Half CV^2     0.00837
90%   3.66e+03       0.87548 Gini coeff.   0.07260
95%   3.83e+03       0.93524 p90/p10       1.39138
99%   4.18e+03       0.98601 p75/p25       1.18986
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_logn_6.pdf saved as PDF format
Distribution of wage in lignite before transition to unemployment for cell 7

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wcoal_end_~y |      3831       3831   2143.823   240728.8   490.6413   2.307691   27.83567    8212987   457.1498   10356.43   1133.637   1546.246   1635.984   1830.076   2096.518   2398.933 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wcoal_end_~y |  2655.587   2898.318   3662.447 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_7.pdf saved as PDF format
file results/two/5_sample2_wcoal_kdensity_7.pdf saved as PDF format
file results/two/5_sample2_wcoal_qnorm_7.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                              ----- Joint test -----
         Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
------------------+-----------------------------------------------------------------
wcoal_end_monthly |     3,831         0.0000         0.0000             .          .

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -421819.84
rescale:       log likelihood = -39721.606
rescale eq:    log likelihood = -31489.556
Iteration 0:   log likelihood = -31489.556  (not concave)
Iteration 1:   log likelihood =  -28942.02  
Iteration 2:   log likelihood = -28938.908  
Iteration 3:   log likelihood = -28938.902  
Iteration 4:   log likelihood = -28938.902  

ML fit of lognormal distribution                  Number of obs   =       3831
                                                  Wald chi2(0)    =          .
Log likelihood = -28938.902                       Prob > chi2     =          .

------------------------------------------------------------------------------
wcoal_end_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.646244   .0035644  2145.14   0.000     7.639257     7.65323
-------------+----------------------------------------------------------------
v            |
       _cons |   .2206218   .0025204    87.53   0.000     .2156819    .2255618
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wcoal_end_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%   1.25e+03       0.00543
 5%   1.46e+03       0.03106
10%   1.58e+03       0.06653
20%   1.74e+03       0.14406
25%   1.80e+03       0.18536
30%   1.86e+03       0.22813
40%   1.98e+03       0.31776 Mode         1.99e+03
50%   2.09e+03       0.41269 Mean         2.14e+03
60%   2.21e+03       0.51305 Std. Dev.   478.90076
70%   2.35e+03       0.61935
75%   2.43e+03       0.67504 Variance     2.29e+05
80%   2.52e+03       0.73270 Half CV^2     0.02494
90%   2.78e+03       0.85564 Gini coeff.   0.12397
95%   3.01e+03       0.92281 p90/p10       1.76029
99%   3.50e+03       0.98239 p75/p25       1.34664
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_logn_7.pdf saved as PDF format
Distribution of wage in lignite before transition to unemployment for cell 8

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wcoal_end_~y |       197        197   2748.408   467685.9   683.8757   .4009609   8.640934   541436.3   639.0529   6656.616   644.4822   1472.002   2021.742   2458.853   2752.806   3106.436 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wcoal_end_~y |  3498.608   3710.136   4456.661 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_8.pdf saved as PDF format
file results/two/5_sample2_wcoal_kdensity_8.pdf saved as PDF format
file results/two/5_sample2_wcoal_qnorm_8.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                              ----- Joint test -----
         Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
------------------+-----------------------------------------------------------------
wcoal_end_monthly |       197         0.0215         0.0000         29.22     0.0000

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -23100.437
rescale:       log likelihood = -2100.1276
rescale eq:    log likelihood = -1619.5712
Iteration 0:   log likelihood = -1619.5712  
Iteration 1:   log likelihood = -1593.8017  
Iteration 2:   log likelihood = -1591.5652  
Iteration 3:   log likelihood = -1591.5534  
Iteration 4:   log likelihood = -1591.5534  

ML fit of lognormal distribution                  Number of obs   =        197
                                                  Wald chi2(0)    =          .
Log likelihood = -1591.5534                       Prob > chi2     =          .

------------------------------------------------------------------------------
wcoal_end_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.881729   .0209982   375.35   0.000     7.840574    7.922885
-------------+----------------------------------------------------------------
v            |
       _cons |   .2947238    .014848    19.85   0.000     .2656223    .3238252
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wcoal_end_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%   1.33e+03       0.00438
 5%   1.63e+03       0.02622
10%   1.82e+03       0.05748
20%   2.07e+03       0.12791
25%   2.17e+03       0.16622
30%   2.27e+03       0.20636
40%   2.46e+03       0.29182 Mode         2.43e+03
50%   2.65e+03       0.38410 Mean         2.77e+03
60%   2.85e+03       0.48350 Std. Dev.   833.23551
70%   3.09e+03       0.59083
75%   3.23e+03       0.64794 Variance     6.94e+05
80%   3.39e+03       0.70778 Half CV^2     0.04537
90%   3.86e+03       0.83814 Gini coeff.   0.16508
95%   4.30e+03       0.91151 p90/p10       2.12848
99%   5.26e+03       0.97890 p75/p25       1.48821
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_logn_8.pdf saved as PDF format
Distribution of wage in lignite before transition to unemployment for cell 9

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wcoal_end_~y |      7239       7239   2283.011     503561   709.6203   3.820086   42.30368   1.65e+07   428.1811    15502.6   1433.289    1563.62   1637.208   1793.159    2191.45   2590.035 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wcoal_end_~y |  2966.963   3395.765   4579.526 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_9.pdf saved as PDF format
file results/two/5_sample2_wcoal_kdensity_9.pdf saved as PDF format
file results/two/5_sample2_wcoal_qnorm_9.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                              ----- Joint test -----
         Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
------------------+-----------------------------------------------------------------
wcoal_end_monthly |     7,239         0.0000         0.0000             .          .

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -808173.45
rescale:       log likelihood = -75510.439
rescale eq:    log likelihood = -59658.925
Iteration 0:   log likelihood = -59658.925  (not concave)
Iteration 1:   log likelihood = -57173.702  
Iteration 2:   log likelihood = -56313.684  
Iteration 3:   log likelihood = -56228.514  
Iteration 4:   log likelihood = -56226.896  
Iteration 5:   log likelihood = -56226.894  

ML fit of lognormal distribution                  Number of obs   =       7239
                                                  Wald chi2(0)    =          .
Log likelihood = -56226.894                       Prob > chi2     =          .

------------------------------------------------------------------------------
wcoal_end_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.696675   .0030518  2521.99   0.000     7.690694    7.702657
-------------+----------------------------------------------------------------
v            |
       _cons |   .2596564    .002158   120.32   0.000     .2554269    .2638859
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wcoal_end_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%   1.20e+03       0.00485
 5%   1.44e+03       0.02842
10%   1.58e+03       0.06163
20%   1.77e+03       0.13539
25%   1.85e+03       0.17511
30%   1.92e+03       0.21650
40%   2.06e+03       0.30397 Mode         2.06e+03
50%   2.20e+03       0.39756 Mean         2.28e+03
60%   2.35e+03       0.49748 Std. Dev.   601.20750
70%   2.52e+03       0.60440
75%   2.62e+03       0.66087 Variance     3.61e+05
80%   2.74e+03       0.71970 Half CV^2     0.03487
90%   3.07e+03       0.84658 Gini coeff.   0.14568
95%   3.37e+03       0.91700 p90/p10       1.94551
99%   4.03e+03       0.98062 p75/p25       1.41945
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_logn_9.pdf saved as PDF format
Distribution of wage in lignite before transition to unemployment for cell 10

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wcoal_end_~y |       264        264   3238.855   813803.5   902.1106   3.063843   18.95812   855057.6   827.8381    9613.32    1817.36   2327.272   2543.659   2758.674   3116.415    3488.85 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wcoal_end_~y |  3970.053   4519.642   7614.854 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_10.pdf saved as PDF format
file results/two/5_sample2_wcoal_kdensity_10.pdf saved as PDF format
file results/two/5_sample2_wcoal_qnorm_10.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                              ----- Joint test -----
         Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
------------------+-----------------------------------------------------------------
wcoal_end_monthly |       264         0.0000         0.0000        150.31     0.0000

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -32335.097
rescale:       log likelihood = -2870.4923
rescale eq:    log likelihood = -2128.5934
Iteration 0:   log likelihood = -2128.5934  
Iteration 1:   log likelihood = -2122.6704  
Iteration 2:   log likelihood = -2122.1443  
Iteration 3:   log likelihood = -2122.1438  
Iteration 4:   log likelihood = -2122.1438  

ML fit of lognormal distribution                  Number of obs   =        264
                                                  Wald chi2(0)    =          .
Log likelihood = -2122.1438                       Prob > chi2     =          .

------------------------------------------------------------------------------
wcoal_end_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   8.052783     .01468   548.56   0.000     8.024011    8.081555
-------------+----------------------------------------------------------------
v            |
       _cons |    .238521   .0103803    22.98   0.000      .218176     .258866
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wcoal_end_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%   1.80e+03       0.00516
 5%   2.12e+03       0.02982
10%   2.31e+03       0.06425
20%   2.57e+03       0.14004
25%   2.68e+03       0.18062
30%   2.77e+03       0.22276
40%   2.96e+03       0.31141 Mode         2.97e+03
50%   3.14e+03       0.40574 Mean         3.23e+03
60%   3.34e+03       0.50591 Std. Dev.   782.28707
70%   3.56e+03       0.61251
75%   3.69e+03       0.66857 Variance     6.12e+05
80%   3.84e+03       0.72678 Half CV^2     0.02927
90%   4.27e+03       0.85153 Gini coeff.   0.13394
95%   4.65e+03       0.92019 p90/p10       1.84292
99%   5.47e+03       0.98159 p75/p25       1.37955
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_logn_10.pdf saved as PDF format
Distribution of wage in lignite before transition to unemployment for cell 11

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wcoal_end_~y |      1966       1966    2300.84    1162452    1078.17   6.124849   76.14568    4523452    959.265      21968   1413.865   1569.486    1643.66   1762.721   1945.759   2506.682 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wcoal_end_~y |  3194.995   4000.426   6637.693 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_11.pdf saved as PDF format
file results/two/5_sample2_wcoal_kdensity_11.pdf saved as PDF format
file results/two/5_sample2_wcoal_qnorm_11.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                              ----- Joint test -----
         Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
------------------+-----------------------------------------------------------------
wcoal_end_monthly |     1,966         0.0000         0.0000             .          .

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -218692.93
rescale:       log likelihood = -20471.623
rescale eq:    log likelihood = -16325.938
Iteration 0:   log likelihood = -16325.938  (not concave)
Iteration 1:   log likelihood = -15722.133  
Iteration 2:   log likelihood = -15599.588  
Iteration 3:   log likelihood = -15596.603  
Iteration 4:   log likelihood = -15596.592  
Iteration 5:   log likelihood = -15596.592  

ML fit of lognormal distribution                  Number of obs   =       1966
                                                  Wald chi2(0)    =          .
Log likelihood = -15596.592                       Prob > chi2     =          .

------------------------------------------------------------------------------
wcoal_end_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.681112   .0070216  1093.93   0.000      7.66735    7.694874
-------------+----------------------------------------------------------------
v            |
       _cons |   .3113334    .004965    62.71   0.000     .3016022    .3210647
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wcoal_end_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%   1.05e+03       0.00417
 5%   1.30e+03       0.02522
10%   1.45e+03       0.05559
20%   1.67e+03       0.12446
25%   1.76e+03       0.16211
30%   1.84e+03       0.20165
40%   2.00e+03       0.28615 Mode         1.97e+03
50%   2.17e+03       0.37777 Mean         2.27e+03
60%   2.34e+03       0.47688 Std. Dev.   725.68313
70%   2.55e+03       0.58436
75%   2.67e+03       0.64176 Variance     5.27e+05
80%   2.82e+03       0.70204 Half CV^2     0.05089
90%   3.23e+03       0.83403 Gini coeff.   0.17424
95%   3.62e+03       0.90882 p90/p10       2.22105
99%   4.47e+03       0.97805 p75/p25       1.52193
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_logn_11.pdf saved as PDF format
Distribution of wage in lignite before transition to unemployment for cell 12

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wcoal_end_~y |       133        133   4206.822   1.40e+07   3742.706   5.041823   33.30255   559507.4   368.5754   33257.08   1815.807   1938.402   2635.576   2975.616   3324.636   3824.495 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wcoal_end_~y |  5405.072   10039.63    20594.5 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_12.pdf saved as PDF format
file results/two/5_sample2_wcoal_kdensity_12.pdf saved as PDF format
file results/two/5_sample2_wcoal_qnorm_12.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                              ----- Joint test -----
         Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
------------------+-----------------------------------------------------------------
wcoal_end_monthly |       133         0.0000         0.0000        120.66     0.0000

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -16896.568
rescale:       log likelihood = -1469.1861
rescale eq:    log likelihood = -1190.9683
Iteration 0:   log likelihood = -1190.9683  
Iteration 1:   log likelihood = -1183.4526  
Iteration 2:   log likelihood = -1181.6611  
Iteration 3:   log likelihood = -1181.6433  
Iteration 4:   log likelihood = -1181.6433  

ML fit of lognormal distribution                  Number of obs   =        133
                                                  Wald chi2(0)    =          .
Log likelihood = -1181.6433                       Prob > chi2     =          .

------------------------------------------------------------------------------
wcoal_end_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   8.186241   .0421796   194.08   0.000      8.10357    8.268911
-------------+----------------------------------------------------------------
v            |
       _cons |   .4864392   .0298255    16.31   0.000     .4279823    .5448961
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wcoal_end_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%   1.16e+03       0.00246
 5%   1.61e+03       0.01653
10%   1.93e+03       0.03853
20%   2.38e+03       0.09208
25%   2.59e+03       0.12284
30%   2.78e+03       0.15605
40%   3.17e+03       0.22971 Mode         2.83e+03
50%   3.59e+03       0.31333 Mean         4.04e+03
60%   4.06e+03       0.40784 Std. Dev.    2.09e+03
70%   4.63e+03       0.51514
75%   4.99e+03       0.57458 Variance     4.36e+06
80%   5.41e+03       0.63877 Half CV^2     0.13348
90%   6.70e+03       0.78673 Gini coeff.   0.26913
95%   7.99e+03       0.87665 p90/p10       3.47917
99%   1.11e+04       0.96711 p75/p25       1.92745
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_logn_12.pdf saved as PDF format
Distribution of wage in lignite before transition to unemployment for cell 13

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wcoal_end_~y |       246        246   1486.536   507267.1   712.2268   .7916737   3.694782   365687.9   444.3176   4057.589   502.0518   598.5478    610.045   816.5005   1515.903   1903.068 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wcoal_end_~y |  2374.296   2574.002   3683.846 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_13.pdf saved as PDF format
file results/two/5_sample2_wcoal_kdensity_13.pdf saved as PDF format
file results/two/5_sample2_wcoal_qnorm_13.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                              ----- Joint test -----
         Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
------------------+-----------------------------------------------------------------
wcoal_end_monthly |       246         0.0000         0.0467         20.88     0.0000

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -23939.975
rescale:       log likelihood = -2414.8489
rescale eq:    log likelihood = -2105.6132
Iteration 0:   log likelihood = -2105.6132  (not concave)
Iteration 1:   log likelihood =  -1953.688  
Iteration 2:   log likelihood = -1944.5835  
Iteration 3:   log likelihood = -1944.4998  
Iteration 4:   log likelihood = -1944.4996  
Iteration 5:   log likelihood = -1944.4996  

ML fit of lognormal distribution                  Number of obs   =        246
                                                  Wald chi2(0)    =          .
Log likelihood = -1944.4996                       Prob > chi2     =          .

------------------------------------------------------------------------------
wcoal_end_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.186271   .0316377   227.14   0.000     7.124262     7.24828
-------------+----------------------------------------------------------------
v            |
       _cons |   .4962181   .0223712    22.18   0.000     .4523713     .540065
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wcoal_end_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  416.50363       0.00238
 5%  584.09579       0.01613
10%  699.48340       0.03772
20%  870.13074       0.09047
25%  945.37073       0.12086
30%   1.02e+03       0.15372
40%   1.17e+03       0.22676 Mode         1.03e+03
50%   1.32e+03       0.30987 Mean         1.49e+03
60%   1.50e+03       0.40405 Std. Dev.   789.55412
70%   1.71e+03       0.51124
75%   1.85e+03       0.57075 Variance     6.23e+05
80%   2.01e+03       0.63510 Half CV^2     0.13960
90%   2.50e+03       0.78387 Gini coeff.   0.27432
95%   2.99e+03       0.87465 p90/p10       3.56748
99%   4.19e+03       0.96638 p75/p25       1.95304
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_logn_13.pdf saved as PDF format
Distribution of wage in lignite before transition to unemployment for cell 14

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   2859.712   196003.3   442.7226  -.4734785    2.03759   14298.56   2201.232   3335.482   2201.232   2201.232   2201.232   2718.728   2860.631   3182.486 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wcoal_end_~y |  3335.482   3335.482   3335.482 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_14.pdf saved as PDF format
file results/two/5_sample2_wcoal_kdensity_14.pdf saved as PDF format
file results/two/5_sample2_wcoal_qnorm_14.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                              ----- Joint test -----
         Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
------------------+-----------------------------------------------------------------
wcoal_end_monthly |        NA             .              .              .          .

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -595.84669
rescale:       log likelihood = -53.706484
rescale eq:    log likelihood = -37.747739
Iteration 0:   log likelihood = -37.747739  
Iteration 1:   log likelihood = -37.229426  
Iteration 2:   log likelihood = -37.193342  
Iteration 3:   log likelihood = -37.193159  
Iteration 4:   log likelihood = -37.193159  

ML fit of lognormal distribution                  Number of obs   =         NA
                                                  Wald chi2(0)    =          .
Log likelihood = -37.193159                       Prob > chi2     =          .

------------------------------------------------------------------------------
wcoal_end_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.948256   .0650056   122.27   0.000     7.820847    8.075665
-------------+----------------------------------------------------------------
v            |
       _cons |    .145357   .0459659     3.16   0.002     .0552654    .2354485
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wcoal_end_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%   2.02e+03       0.00672
 5%   2.23e+03       0.03671
10%   2.35e+03       0.07680
20%   2.50e+03       0.16183
25%   2.57e+03       0.20615
30%   2.62e+03       0.25151
40%   2.73e+03       0.34506 Mode         2.77e+03
50%   2.83e+03       0.44221 Mean         2.86e+03
60%   2.94e+03       0.54300 Std. Dev.   418.02827
70%   3.05e+03       0.64767
75%   3.12e+03       0.70164 Variance     1.75e+05
80%   3.20e+03       0.75687 Half CV^2     0.01068
90%   3.41e+03       0.87206 Gini coeff.   0.08186
95%   3.60e+03       0.93313 p90/p10       1.45145
99%   3.97e+03       0.98541 p75/p25       1.21663
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_logn_14.pdf saved as PDF format
Distribution of wage in lignite before transition to unemployment for cell 15

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wcoal_end_~y |       374        374   1859.363   199544.8   446.7043   1.353459   11.21456   695401.7   437.9526   5115.441   894.2588   1191.501    1402.25   1570.806   1815.309   2124.712 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wcoal_end_~y |  2358.883   2552.197   3052.657 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_15.pdf saved as PDF format
file results/two/5_sample2_wcoal_kdensity_15.pdf saved as PDF format
file results/two/5_sample2_wcoal_qnorm_15.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                              ----- Joint test -----
         Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
------------------+-----------------------------------------------------------------
wcoal_end_monthly |       374         0.0000         0.0000        105.46     0.0000

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -39582.774
rescale:       log likelihood = -3810.9108
rescale eq:    log likelihood = -3120.2776
Iteration 0:   log likelihood = -3120.2776  (not concave)
Iteration 1:   log likelihood = -2896.2401  (not concave)
Iteration 2:   log likelihood = -2815.0026  
Iteration 3:   log likelihood =  -2804.901  
Iteration 4:   log likelihood = -2804.7157  
Iteration 5:   log likelihood = -2804.7152  
Iteration 6:   log likelihood = -2804.7152  

ML fit of lognormal distribution                  Number of obs   =        374
                                                  Wald chi2(0)    =          .
Log likelihood = -2804.7152                       Prob > chi2     =          .

------------------------------------------------------------------------------
wcoal_end_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.499777   .0125053   599.73   0.000     7.475267    7.524287
-------------+----------------------------------------------------------------
v            |
       _cons |   .2418405   .0088426    27.35   0.000     .2245094    .2591716
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wcoal_end_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%   1.03e+03       0.00511
 5%   1.21e+03       0.02960
10%   1.33e+03       0.06383
20%   1.47e+03       0.13930
25%   1.54e+03       0.17975
30%   1.59e+03       0.22177
40%   1.70e+03       0.31023 Mode         1.70e+03
50%   1.81e+03       0.40445 Mean         1.86e+03
60%   1.92e+03       0.50459 Std. Dev.   456.79578
70%   2.05e+03       0.61124
75%   2.13e+03       0.66737 Variance     2.09e+05
80%   2.22e+03       0.72567 Half CV^2     0.03012
90%   2.46e+03       0.85076 Gini coeff.   0.13578
95%   2.69e+03       0.91969 p90/p10       1.85867
99%   3.17e+03       0.98144 p75/p25       1.38575
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_logn_15.pdf saved as PDF format
Distribution of wage in lignite before transition to unemployment for cell 16

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   1676.257   281669.5   530.7254   .0774426   1.405256   10057.54   1031.367   2351.817   1031.367   1031.367   1031.367   1310.262   1614.773   2134.548 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wcoal_end_~y |  2351.817   2351.817   2351.817 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_16.pdf saved as PDF format
file results/two/5_sample2_wcoal_kdensity_16.pdf saved as PDF format
file results/two/5_sample2_wcoal_qnorm_16.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                              ----- Joint test -----
         Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
------------------+-----------------------------------------------------------------
wcoal_end_monthly |        NA             .              .              .          .

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -614.81946
rescale:       log likelihood = -60.274423
rescale eq:    log likelihood = -51.216537
Iteration 0:   log likelihood = -51.216537  (not concave)
Iteration 1:   log likelihood = -46.641656  
Iteration 2:   log likelihood = -45.681313  
Iteration 3:   log likelihood =  -45.55535  
Iteration 4:   log likelihood =  -45.55535  

ML fit of lognormal distribution                  Number of obs   =         NA
                                                  Wald chi2(0)    =          .
Log likelihood =  -45.55535                       Prob > chi2     =          .

------------------------------------------------------------------------------
wcoal_end_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.380575   .1221098    60.44   0.000     7.141244    7.619905
-------------+----------------------------------------------------------------
v            |
       _cons |   .2991067   .0863447     3.46   0.001     .1298743    .4683392
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wcoal_end_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  800.10928       0.00433
 5%  981.01509       0.02595
10%   1.09e+03       0.05698
20%   1.25e+03       0.12699
25%   1.31e+03       0.16513
30%   1.37e+03       0.20511
40%   1.49e+03       0.29032 Mode         1.47e+03
50%   1.60e+03       0.38243 Mean         1.68e+03
60%   1.73e+03       0.48175 Std. Dev.   513.31270
70%   1.88e+03       0.58912
75%   1.96e+03       0.64631 Variance     2.63e+05
80%   2.06e+03       0.70627 Half CV^2     0.04679
90%   2.35e+03       0.83706 Gini coeff.   0.16750
95%   2.62e+03       0.91081 p90/p10       2.15252
99%   3.22e+03       0.97868 p75/p25       1.49704
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_logn_16.pdf saved as PDF format
Distribution of wage in lignite before transition to unemployment for cell 17

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wcoal_end_~y |       121        121   1929.828     140307   374.5757   .2637499   3.124806   233509.1   954.3232   3079.042   1167.452   1327.583   1517.962   1654.136   1905.393   2185.542 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wcoal_end_~y |  2335.325   2622.269   2721.254 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_17.pdf saved as PDF format
file results/two/5_sample2_wcoal_kdensity_17.pdf saved as PDF format
file results/two/5_sample2_wcoal_qnorm_17.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                              ----- Joint test -----
         Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
------------------+-----------------------------------------------------------------
wcoal_end_monthly |       121         0.2182         0.5606          1.89     0.3883

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -12964.351
rescale:       log likelihood =  -1239.698
rescale eq:    log likelihood = -999.75582
Iteration 0:   log likelihood = -999.75582  (not concave)
Iteration 1:   log likelihood =   -918.233  (not concave)
Iteration 2:   log likelihood = -892.30021  
Iteration 3:   log likelihood = -888.90166  
Iteration 4:   log likelihood = -888.83377  
Iteration 5:   log likelihood = -888.83357  
Iteration 6:   log likelihood = -888.83357  

ML fit of lognormal distribution                  Number of obs   =        121
                                                  Wald chi2(0)    =          .
Log likelihood = -888.83357                       Prob > chi2     =          .

------------------------------------------------------------------------------
wcoal_end_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.546032   .0180045   419.12   0.000     7.510743     7.58132
-------------+----------------------------------------------------------------
v            |
       _cons |   .1980495   .0127311    15.56   0.000      .173097    .2230021
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wcoal_end_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%   1.19e+03       0.00579
 5%   1.37e+03       0.03267
10%   1.47e+03       0.06949
20%   1.60e+03       0.14925
25%   1.66e+03       0.19146
30%   1.71e+03       0.23501
40%   1.80e+03       0.32585 Mode         1.82e+03
50%   1.89e+03       0.42150 Mean         1.93e+03
60%   1.99e+03       0.52205 Std. Dev.   386.15673
70%   2.10e+03       0.62792
75%   2.16e+03       0.68312 Variance     1.49e+05
80%   2.24e+03       0.74007 Half CV^2     0.02000
90%   2.44e+03       0.86071 Gini coeff.   0.11137
95%   2.62e+03       0.92602 p90/p10       1.66133
99%   3.00e+03       0.98334 p75/p25       1.30626
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_logn_17.pdf saved as PDF format
Distribution of wage in lignite before transition to unemployment for cell 18

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   2143.846          .          .          .          .   2143.846   2143.846   2143.846   2143.846   2143.846   2143.846   2143.846   2143.846   2143.846 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wcoal_end_~y |  2143.846   2143.846   2143.846 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_18.pdf saved as PDF format
insufficient observations
no observations

Skewness and kurtosis tests for normality
                                                              ----- Joint test -----
         Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
------------------+-----------------------------------------------------------------
wcoal_end_monthly |        NA             .              .              .          .

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -110.72417
rescale:       log likelihood = -10.396574
rescale eq:    log likelihood = -8.0723191
insufficient observations
file results/two/5_sample2_wcoal_dpplot_logn_18.pdf saved as PDF format
Distribution of wage in lignite before transition to unemployment for cell 19

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wcoal_end_~y |      1836       1836   1991.373   380097.9   616.5208   5.562154   96.96482    3656161   332.6837   14385.98   725.2584   1293.768   1454.337    1682.34   1921.955   2246.019 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wcoal_end_~y |  2562.561   2794.948   3742.042 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_19.pdf saved as PDF format
file results/two/5_sample2_wcoal_kdensity_19.pdf saved as PDF format
file results/two/5_sample2_wcoal_qnorm_19.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                              ----- Joint test -----
         Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
------------------+-----------------------------------------------------------------
wcoal_end_monthly |     1,836         0.0000         0.0000             .          .

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -197476.26
rescale:       log likelihood = -18838.461
rescale eq:    log likelihood = -15306.032
Iteration 0:   log likelihood = -15306.032  (not concave)
Iteration 1:   log likelihood = -14300.654  
Iteration 2:   log likelihood =  -14185.19  
Iteration 3:   log likelihood = -14171.212  
Iteration 4:   log likelihood = -14171.134  
Iteration 5:   log likelihood = -14171.134  

ML fit of lognormal distribution                  Number of obs   =       1836
                                                  Wald chi2(0)    =          .
Log likelihood = -14171.134                       Prob > chi2     =          .

------------------------------------------------------------------------------
wcoal_end_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.557375   .0066343  1139.14   0.000     7.544372    7.570378
-------------+----------------------------------------------------------------
v            |
       _cons |   .2842699   .0046912    60.60   0.000     .2750754    .2934644
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wcoal_end_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  988.37737       0.00452
 5%   1.20e+03       0.02686
10%   1.33e+03       0.05870
20%   1.51e+03       0.13011
25%   1.58e+03       0.16884
30%   1.65e+03       0.20935
40%   1.78e+03       0.29542 Mode         1.77e+03
50%   1.91e+03       0.38810 Mean         1.99e+03
60%   2.06e+03       0.48767 Std. Dev.   578.41245
70%   2.22e+03       0.59489
75%   2.32e+03       0.65181 Variance     3.35e+05
80%   2.43e+03       0.71136 Half CV^2     0.04208
90%   2.76e+03       0.84069 Gini coeff.   0.15931
95%   3.06e+03       0.91318 p90/p10       2.07220
99%   3.71e+03       0.97943 p75/p25       1.46737
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_logn_19.pdf saved as PDF format
Distribution of wage in lignite before transition to unemployment for cell 20

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   2812.821   445645.7    667.567  -.4118271    3.94098   92823.08   990.4165   4315.429   990.4165   1557.373   1940.885   2580.615   2799.747   3103.972 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wcoal_end_~y |  3486.525   4012.082   4315.429 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_20.pdf saved as PDF format
file results/two/5_sample2_wcoal_kdensity_20.pdf saved as PDF format
file results/two/5_sample2_wcoal_qnorm_20.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                              ----- Joint test -----
         Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
------------------+-----------------------------------------------------------------
wcoal_end_monthly |        NA         0.2737         0.1291          3.78     0.1509

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -3895.8195
rescale:       log likelihood = -352.87984
rescale eq:    log likelihood = -268.08902
Iteration 0:   log likelihood = -268.08902  
Iteration 1:   log likelihood = -265.62437  
Iteration 2:   log likelihood = -265.48757  
Iteration 3:   log likelihood = -265.48744  
Iteration 4:   log likelihood = -265.48744  

ML fit of lognormal distribution                  Number of obs   =         NA
                                                  Wald chi2(0)    =          .
Log likelihood = -265.48744                       Prob > chi2     =          .

------------------------------------------------------------------------------
wcoal_end_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.908342   .0482934   163.76   0.000     7.813689    8.002996
-------------+----------------------------------------------------------------
v            |
       _cons |   .2774244   .0341486     8.12   0.000     .2104944    .3443543
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wcoal_end_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%   1.43e+03       0.00461
 5%   1.72e+03       0.02729
10%   1.91e+03       0.05950
20%   2.15e+03       0.13156
25%   2.26e+03       0.17057
30%   2.35e+03       0.21133
40%   2.54e+03       0.29779 Mode         2.52e+03
50%   2.72e+03       0.39073 Mean         2.83e+03
60%   2.92e+03       0.49040 Std. Dev.   799.49639
70%   3.15e+03       0.59754
75%   3.28e+03       0.65434 Variance     6.39e+05
80%   3.44e+03       0.71369 Half CV^2     0.04000
90%   3.88e+03       0.84234 Gini coeff.   0.15552
95%   4.29e+03       0.91425 p90/p10       2.03616
99%   5.19e+03       0.97977 p75/p25       1.45389
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_logn_20.pdf saved as PDF format
Distribution of wage in lignite before transition to unemployment for cell 21

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wcoal_end_~y |      3799       3799   2150.376     443109   665.6643   2.939485   24.24114    8169279   320.4664   9489.188   1072.308   1422.856   1522.826   1734.089   2069.515   2416.545 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wcoal_end_~y |  2865.998   3208.591   4274.453 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_21.pdf saved as PDF format
file results/two/5_sample2_wcoal_kdensity_21.pdf saved as PDF format
file results/two/5_sample2_wcoal_qnorm_21.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                              ----- Joint test -----
         Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
------------------+-----------------------------------------------------------------
wcoal_end_monthly |     3,799         0.0000         0.0000             .          .

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -417183.01
rescale:       log likelihood = -39338.594
rescale eq:    log likelihood = -31438.483
Iteration 0:   log likelihood = -31438.483  (not concave)
Iteration 1:   log likelihood = -29480.932  
Iteration 2:   log likelihood =  -29449.66  
Iteration 3:   log likelihood = -29449.301  
Iteration 4:   log likelihood = -29449.301  

ML fit of lognormal distribution                  Number of obs   =       3799
                                                  Wald chi2(0)    =          .
Log likelihood = -29449.301                       Prob > chi2     =          .

------------------------------------------------------------------------------
wcoal_end_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.634615   .0044141  1729.58   0.000     7.625963    7.643266
-------------+----------------------------------------------------------------
v            |
       _cons |   .2720696   .0031213    87.17   0.000     .2659521    .2781872
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wcoal_end_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%   1.10e+03       0.00468
 5%   1.32e+03       0.02762
10%   1.46e+03       0.06014
20%   1.65e+03       0.13271
25%   1.72e+03       0.17193
30%   1.79e+03       0.21288
40%   1.93e+03       0.29965 Mode         1.92e+03
50%   2.07e+03       0.39278 Mean         2.15e+03
60%   2.22e+03       0.49253 Std. Dev.   594.99200
70%   2.39e+03       0.59961
75%   2.49e+03       0.65631 Variance     3.54e+05
80%   2.60e+03       0.71551 Half CV^2     0.03842
90%   2.93e+03       0.84363 Gini coeff.   0.15256
95%   3.24e+03       0.91509 p90/p10       2.00841
99%   3.90e+03       0.98003 p75/p25       1.44342
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_logn_21.pdf saved as PDF format
Distribution of wage in lignite before transition to unemployment for cell 22

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wcoal_end_~y |        NA         NA   3885.348    6168160   2483.578   1.753464   6.613999   54394.88   1020.917   11212.72   1020.917   1020.917   1071.527   2455.461   3926.208   4622.802 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wcoal_end_~y |  4918.497   11212.72   11212.72 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_22.pdf saved as PDF format
file results/two/5_sample2_wcoal_kdensity_22.pdf saved as PDF format
file results/two/5_sample2_wcoal_qnorm_22.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                              ----- Joint test -----
         Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
------------------+-----------------------------------------------------------------
wcoal_end_monthly |        NA         0.0028         0.0038         12.68     0.0018

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -1740.1575
rescale:       log likelihood = -153.02717
rescale eq:    log likelihood = -127.04277
Iteration 0:   log likelihood = -127.04277  
Iteration 1:   log likelihood = -126.20314  
Iteration 2:   log likelihood =  -126.1898  
Iteration 3:   log likelihood = -126.18977  
Iteration 4:   log likelihood = -126.18977  

ML fit of lognormal distribution                  Number of obs   =         NA
                                                  Wald chi2(0)    =          .
Log likelihood = -126.18977                       Prob > chi2     =          .

------------------------------------------------------------------------------
wcoal_end_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   8.090749   .1627303    49.72   0.000     7.771804    8.409695
-------------+----------------------------------------------------------------
v            |
       _cons |   .6088809   .1150677     5.29   0.000     .3833524    .8344094
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wcoal_end_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  791.77749       0.00167
 5%   1.20e+03       0.01211
10%   1.50e+03       0.02935
20%   1.96e+03       0.07346
25%   2.16e+03       0.09968
30%   2.37e+03       0.12855
40%   2.80e+03       0.19428 Mode         2.25e+03
50%   3.26e+03       0.27130 Mean         3.93e+03
60%   3.81e+03       0.36109 Std. Dev.    2.63e+03
70%   4.49e+03       0.46634
75%   4.92e+03       0.52616 Variance     6.93e+06
80%   5.45e+03       0.59202 Half CV^2     0.22440
90%   7.12e+03       0.74942 Gini coeff.   0.33320
95%   8.89e+03       0.84989 p90/p10       4.76179
99%   1.35e+04       0.95705 p75/p25       2.27361
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_logn_22.pdf saved as PDF format
Distribution of wage in lignite before transition to unemployment for cell 23

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wcoal_end_~y |       641        641   2190.869   426484.4   653.0577   1.394857   6.295035    1404347   775.4878   6103.044   1066.394   1422.856   1563.432   1757.199   2054.313   2469.159 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wcoal_end_~y |  3069.746   3439.358   4368.066 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_23.pdf saved as PDF format
file results/two/5_sample2_wcoal_kdensity_23.pdf saved as PDF format
file results/two/5_sample2_wcoal_qnorm_23.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                              ----- Joint test -----
         Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
------------------+-----------------------------------------------------------------
wcoal_end_monthly |       641         0.0000         0.0000        137.65     0.0000

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -70732.856
rescale:       log likelihood =  -6651.662
rescale eq:    log likelihood = -5303.0903
Iteration 0:   log likelihood = -5303.0903  (not concave)
Iteration 1:   log likelihood = -5011.6183  
Iteration 2:   log likelihood = -4992.0678  
Iteration 3:   log likelihood =  -4991.664  
Iteration 4:   log likelihood = -4991.6627  
Iteration 5:   log likelihood = -4991.6627  

ML fit of lognormal distribution                  Number of obs   =        641
                                                  Wald chi2(0)    =          .
Log likelihood = -4991.6627                       Prob > chi2     =          .

------------------------------------------------------------------------------
wcoal_end_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.652484   .0109367   699.71   0.000     7.631048    7.673919
-------------+----------------------------------------------------------------
v            |
       _cons |    .276895   .0077334    35.81   0.000     .2617378    .2920522
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wcoal_end_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%   1.11e+03       0.00462
 5%   1.34e+03       0.02732
10%   1.48e+03       0.05956
20%   1.67e+03       0.13167
25%   1.75e+03       0.17070
30%   1.82e+03       0.21148
40%   1.96e+03       0.29797 Mode         1.95e+03
50%   2.11e+03       0.39093 Mean         2.19e+03
60%   2.26e+03       0.49061 Std. Dev.   617.69359
70%   2.43e+03       0.59774
75%   2.54e+03       0.65454 Variance     3.82e+05
80%   2.66e+03       0.71387 Half CV^2     0.03984
90%   3.00e+03       0.84247 Gini coeff.   0.15523
95%   3.32e+03       0.91434 p90/p10       2.03340
99%   4.01e+03       0.97979 p75/p25       1.45285
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_logn_23.pdf saved as PDF format
Distribution of wage in lignite before transition to unemployment for cell 24

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wcoal_end_~y |         NA          NA   4825.195   1.64e+07   4046.766   2.170436   5.886152   38601.56   2873.238   14727.79   2873.238   2873.238   2873.238   2942.807   3247.115   4310.346 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wcoal_end_~y |  14727.79   14727.79   14727.79 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_24.pdf saved as PDF format
file results/two/5_sample2_wcoal_kdensity_24.pdf saved as PDF format
file results/two/5_sample2_wcoal_qnorm_24.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                              ----- Joint test -----
         Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
------------------+-----------------------------------------------------------------
wcoal_end_monthly |         NA         0.0006         0.0021         13.43     0.0012

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -1047.0979
rescale:       log likelihood = -89.579117
rescale eq:    log likelihood = -73.939075
Iteration 0:   log likelihood = -73.939075  
Iteration 1:   log likelihood = -73.203361  
Iteration 2:   log likelihood = -72.469932  
Iteration 3:   log likelihood = -72.447154  
Iteration 4:   log likelihood = -72.447152  

ML fit of lognormal distribution                  Number of obs   =         NA
                                                  Wald chi2(0)    =          .
Log likelihood = -72.447152                       Prob > chi2     =          .

------------------------------------------------------------------------------
wcoal_end_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   8.304828   .1813015    45.81   0.000     7.949484    8.660173
-------------+----------------------------------------------------------------
v            |
       _cons |   .5127981   .1281994     4.00   0.000     .2615318    .7640643
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wcoal_end_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%   1.23e+03       0.00226
 5%   1.74e+03       0.01548
10%   2.10e+03       0.03638
20%   2.63e+03       0.08780
25%   2.86e+03       0.11756
30%   3.09e+03       0.14982
40%   3.55e+03       0.22179 Mode         3.11e+03
50%   4.04e+03       0.30405 Mean         4.61e+03
60%   4.60e+03       0.39764 Std. Dev.    2.53e+03
70%   5.29e+03       0.50463
75%   5.71e+03       0.56423 Variance     6.40e+06
80%   6.23e+03       0.62886 Half CV^2     0.15039
90%   7.80e+03       0.77898 Gini coeff.   0.28310
95%   9.40e+03       0.87119 p90/p10       3.72235
99%   1.33e+04       0.96513 p75/p25       1.99722
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wcoal_dpplot_logn_24.pdf saved as PDF format
r; t=1075.59 15:32:20

. 
.                 
. *(9.2) Average offer wage after unemployment (in non-lignite only) - Characterize distribution
. di "Number of spells after unemployment (in non-lignite only)"
Number of spells after unemployment (in non-lignite only)
r; t=0.00 15:32:20

. count if posttrans[_n-1] == 7 & pid == pid[_n-1]
  NA
  r; t=0.25 15:32:20

. 
.         * generate w' (woffer) for each suitable spell
.         * full sample
.         sort pid begepi
r; t=0.49 15:32:21

.         cap drop wnc_offer_tentg_beg wnc_offer_suminc_R wnc_offer_beg_monthly
r; t=0.00 15:32:21

. 
.         gen wnc_offer_suminc_R = .
(1,027,536 missing values generated)
r; t=0.02 15:32:21

.         sort pid begepi
r; t=0.15 15:32:21

.         replace wnc_offer_suminc_R = suminc_R if posttrans[_n-1] == 7 & pid == pid[_n-1]
(15,390 real changes made)
r; t=0.30 15:32:21

. 
.         gen wnc_offer_tentg_beg = .
(1,027,536 missing values generated)
r; t=0.03 15:32:21

.         sort pid begepi
r; t=0.18 15:32:22

.         replace wnc_offer_tentg_beg = tentg_beg if posttrans[_n-1] == 7 & pid == pid[_n-1]
(15,390 real changes made)
r; t=0.33 15:32:22

.         
.         gen wnc_offer_beg_monthly = .
(1,027,536 missing values generated)
r; t=0.02 15:32:22

.         sort pid begepi
r; t=0.19 15:32:22

.         replace wnc_offer_beg_monthly = tentg_beg*tentgeltdays if posttrans[_n-1] == 7 & pid == pid[_n-1]
(15,390 real changes made)
r; t=0.30 15:32:22

.         label var wnc_offer_beg_monthly "monthly starting wage in non-coal"
r; t=0.00 15:32:22

. 
.         * create same wnc variable (non-coal starting wages after unemp) for pre-imputed wages:
.         gen wnctentbeg_preimp = .
(1,027,536 missing values generated)
r; t=0.03 15:32:22

.         sort pid begepi
r; t=0.17 15:32:23

.         replace wnctentbeg_preimp = tentbeg_preimp if posttrans[_n-1] == 7 & pid == pid[_n-1]
(15,390 real changes made)
r; t=0.33 15:32:23

.         label var wnctentbeg_preimp "monthly start wage in non-coal pre-top-code-impute"        
r; t=0.00 15:32:23

. 
.         * compare wnc pre- and post-imputation
.         sum wnctentbeg_preimp wnc_offer_beg_monthly, d

     monthly start wage in non-coal pre-top-code-impute
-------------------------------------------------------------
      Percentiles      Smallest
 1%     576.5681       257.8651
 5%     884.0388       266.8943
10%     1080.052       271.7984       Obs              15,390
25%     1289.792       279.8522       Sum of wgt.      15,390

50%     1518.592                      Mean           1596.102
                        Largest       Std. dev.      518.6002
75%      1817.29       5627.817
90%     2217.924       5665.241       Variance       268946.2
95%     2515.779       5868.855       Skewness       1.450324
99%     3347.113       6360.506       Kurtosis       8.604511

              monthly starting wage in non-coal
-------------------------------------------------------------
      Percentiles      Smallest
 1%     576.5681       257.8651
 5%     884.0388       266.8943
10%     1080.052       271.7984       Obs              15,390
25%     1289.792       279.8522       Sum of wgt.      15,390

50%     1518.592                      Mean           1600.073
                        Largest       Std. dev.      560.1966
75%      1817.29       8211.471
90%     2217.924       8334.796       Variance       313820.2
95%     2515.779       11651.15       Skewness       4.447437
99%     3347.268       20319.14       Kurtosis       99.25689
r; t=0.53 15:32:24

.         
.         * full sample
.         di "Distribution of wage offer no coal (w') after unemployment (new job in non-lignite) with suminc_R"
Distribution of wage offer no coal (w') after unemployment (new job in non-lignite) with suminc_R
r; t=0.00 15:32:24

.         sum wnc_offer_suminc_R, d       

                     wnc_offer_suminc_R
-------------------------------------------------------------
      Percentiles      Smallest
 1%     578.6578       257.8651
 5%     886.7463       266.8943
10%     1081.464       271.7984       Obs              15,390
25%     1292.153       279.8522       Sum of wgt.      15,390

50%     1522.922                      Mean           1615.798
                        Largest       Std. dev.       563.871
75%     1830.635       6360.506
90%     2258.793       7396.307       Variance       317950.5
95%     2599.239        7554.33       Skewness       1.971262
99%     3551.823       8591.533       Kurtosis       12.77683
r; t=0.25 15:32:24

.         
.         di "Distribution of wage offer no coal (w') after unemployment (new job in non-lignite) with tentg_beg daily"
Distribution of wage offer no coal (w') after unemployment (new job in non-lignite) with tentg_beg daily
r; t=0.00 15:32:24

.         sum wnc_offer_tentg_beg, d

                     wnc_offer_tentg_beg
-------------------------------------------------------------
      Percentiles      Smallest
 1%     18.93491       8.468474
 5%     29.03247       8.765002
10%     35.46967       8.926055       Obs              15,390
25%      42.3577       9.190549       Sum of wgt.      15,390

50%     49.87167                      Mean           52.54757
                        Largest       Std. dev.      18.39726
75%     59.68112       269.6706
90%     72.83824       273.7207       Variance       338.4592
95%        82.62       382.6323       Skewness       4.447437
99%     109.9267       667.2952       Kurtosis       99.25688
r; t=0.28 15:32:24

.         
.         di "Distribution of wage offer no coal (w') after unemployment (new job in non-lignite) with tentg_beg monthly"
Distribution of wage offer no coal (w') after unemployment (new job in non-lignite) with tentg_beg monthly
r; t=0.00 15:32:24

.         *monthly wage by year
.         tab jahrend if wnc_offer_beg_monthly!=., matrow(matname) // get years where we have observations

year at end |
   of spell |      Freq.     Percent        Cum.
------------+-----------------------------------
       1978 |         NA        0.01        0.01
       1979 |         NA        0.12        0.12
       1980 |         NA        0.12        0.25
       1981 |         NA        0.25        0.49
       1982 |         NA        0.33        0.83
       1983 |        156        1.01        1.84
       1984 |        105        0.68        2.52
       1985 |         NA        0.49        3.01
       1986 |         NA        0.20        3.21
       1987 |         NA        0.30        3.51
       1988 |         NA        0.18        3.69
       1989 |         NA        0.24        3.93
       1990 |         NA        0.25        4.18
       1991 |         NA        0.16        4.33
       1992 |        183        1.19        5.52
       1993 |      1,363        8.86       14.38
       1994 |      2,269       14.74       29.12
       1995 |      2,131       13.85       42.97
       1996 |      2,259       14.68       57.65
       1997 |      1,744       11.33       68.98
       1998 |      1,002        6.51       75.49
       1999 |        841        5.46       80.96
       2000 |        659        4.28       85.24
       2001 |        443        2.88       88.12
       2002 |        310        2.01       90.13
       2003 |        236        1.53       91.66
       2004 |        168        1.09       92.76
       2005 |        142        0.92       93.68
       2006 |        105        0.68       94.36
       2007 |         NA        0.60       94.96
       2008 |         NA        0.46       95.43
       2009 |         NA        0.43       95.85
       2010 |         NA        0.38       96.24
       2011 |         NA        0.42       96.65
       2012 |         NA        0.32       96.97
       2013 |         NA        0.31       97.28
       2014 |         NA        0.23       97.52
       2015 |         NA        0.29       97.80
       2016 |         NA        0.25       98.05
       2017 |        300        1.95      100.00
------------+-----------------------------------
      Total |     15,390      100.00
r; t=0.21 15:32:24

.         matrix list matname

matname[40,1]
       c1
 r1  1978
 r2  1979
 r3  1980
 r4  1981
 r5  1982
 r6  1983
 r7  1984
 r8  1985
 r9  1986
r10  1987
r11  1988
r12  1989
r13  1990
r14  1991
r15  1992
r16  1993
r17  1994
r18  1995
r19  1996
r20  1997
r21  1998
r22  1999
r23  2000
r24  2001
r25  2002
r26  2003
r27  2004
r28  2005
r29  2006
r30  2007
r31  2008
r32  2009
r33  2010
r34  2011
r35  2012
r36  2013
r37  2014
r38  2015
r39  2016
r40  2017
r; t=0.01 15:32:24

.         local dim = rowsof(matname)
r; t=0.00 15:32:24

.         display `dim'
40
r; t=0.00 15:32:24

.         
.         global year
r; t=0.00 15:32:24

.         
.         forvalues i=1/`dim' {
  2.                 global year $year matname[`i',1]
  3.         }
r; t=0.00 15:32:24

.         display $year
1978197919801981198219831984198519861987198819891990199119921993199419951996199719981999200020012002200320042005200620072008200920102011201220132014201520162017
r; t=0.00 15:32:24

.         
.         foreach i of global year {
  2.                 local j = `i'-1973
  3.                 capture noisily estpost sum wnc_offer_tentg_beg if jahrend==`i'
  4.                 if _rc!=2000{
  5.                         putexcel set results/${samplefolder}/5_sample${sample}_wages_distribution, sheet("daily_wage_byyear") modify
  6.                         putexcel A`j'=(`i') D1=("wnc_offer_daily") D`j'=matrix(e(mean))
  7.                         `putexcelclose'
  8.                 }
  9.                 capture noisily estpost sum wnc_offer_beg_monthly if jahrend==`i'
 10.                 if _rc!=2000{
 11.                         putexcel set results/${samplefolder}/5_sample${sample}_wages_distribution, sheet("monthly_wage_byyear") modify
 12.                         putexcel A`j'=(`i') D1=("wnc_offer_monthly") D`j'=matrix(e(mean))
 13.                         `putexcelclose'
 14.                 }
 15.         }

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |         NA          NA   50.71106          .          .   50.71106   50.71106   50.71106 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |         NA          NA   1544.152          .          .   1544.152   1544.152   1544.152 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |        NA         NA   59.38365   226.2436    15.0414   18.52053   80.69811   1068.906 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   1808.232   209773.6   458.0105   563.9502   2457.258   32548.18 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |        NA         NA   66.36457   487.9236   22.08899    31.5909   128.5083   1260.927 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   2020.801   452404.1   672.6099   961.9428   3913.079   38395.22 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |        NA         NA   70.23295   294.6266   17.16469   18.64605   98.78648   2668.852 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   2138.593   273178.5   522.6648   567.7723   3008.049   81266.55 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |        NA         NA   68.46275   437.0058   20.90468   30.18623   129.5043     3491.6 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   2084.691   405192.9   636.5476   919.1706   3943.405   106319.2 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |       156        156   70.34656   372.7977   19.30797   10.84309   207.6409   10974.06 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |       156        156   2142.053   345658.9   587.9277    330.172   6322.666   334160.3 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |       105        105   66.29771   343.9106   18.54483   27.64705     176.94    6961.26 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |       105        105   2018.765   318874.8     564.69   841.8528   5387.824   211970.4 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |        NA         NA   66.74249   226.6468   15.05479   32.75042   110.8652   5005.687 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   2032.309   210147.5   458.4185   997.2502   3375.844   152423.2 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |        NA         NA    69.1596   258.9363    16.0915   18.67777   91.40614   2143.948 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA    2105.91   240086.4   489.9861    568.738   2783.317   65283.21 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |        NA         NA   69.89312   438.7414   20.94616   27.97109   114.1509   3215.084 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   2128.246   406802.1   637.8104   851.7198   3475.896    97899.3 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |        NA         NA   75.72462   462.2419   21.49981   42.63256   141.5796   2120.289 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   2305.815   428591.9   654.6693   1298.161   4311.098   64562.81 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |        NA         NA   77.59129   606.8652   24.63463   41.86713   180.6496   2870.878 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   2362.655     562687   750.1246   1274.854   5500.781   87418.23 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |        NA         NA   75.36159   600.1669    24.4983   20.48921   121.8621   2863.741 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   2294.761   556476.3   745.9734   623.8964   3710.702    87200.9 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |        NA         NA   74.56297   1097.084   33.12226   23.93311   143.8795   1789.511 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   2270.443    1017219   1008.573   728.7631    4381.13   54490.62 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |       183        183   61.21842   596.0612   24.41436   11.41746   183.3963   11202.97 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |       183        183   1864.101   552669.4   743.4174   347.6617   5584.417   341130.5 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |      1363       1363   53.05836   348.2979   18.66274   8.468474   239.5727   72318.54 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |      1363       1363   1615.627   322942.7   568.2805   257.8651    7294.99    2202100 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |      2269       2269   49.77699   201.9537   14.21104   8.765002   210.1262     112944 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |      2269       2269   1515.709     187252   432.7263   266.8943   6398.343    3439145 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |      2131       2131    50.1354   233.0043   15.26448   9.190549   159.6043   106838.5 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |      2131       2131   1526.623   216042.2   464.8034   279.8522   4859.951    3253234 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |      2259       2259    51.2011   253.1008   15.90914   9.569314   175.2741   115663.3 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |      2259       2259   1559.074   234675.7   484.4333   291.3856   5337.095    3521947 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |      1744       1744   52.31402   225.0403   15.00134   10.20042   269.6706   91235.66 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |      1744       1744   1592.962   208657.9   456.7909   310.6029   8211.471    2778126 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |      1002       1002   51.61572   270.7773   16.45531   10.50852   200.0511   51718.95 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |      1002       1002   1571.699   251065.4   501.0642   319.9844   6091.557    1574842 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |       841        841    51.3233   251.9591   15.87322   10.11595   158.0828   43162.89 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |       841        841   1562.794   233617.2   483.3396   308.0307   4813.621    1314310 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |       659        659   51.57121   300.2393   17.32741   12.39206   184.8859   33985.43 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |       659        659   1570.343   278382.6   527.6198   377.3383   5629.776    1034856 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |       443        443   51.88018    259.501   16.10904   9.382148   142.9357   22982.92 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |       443        443   1579.751     240610   490.5201   285.6864   4352.391   699829.9 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |       310        310   51.80894   254.3225   15.94749   12.00903   126.6098   16060.77 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |       310        310   1577.582   235808.5   485.6011   365.6749   3855.269   489050.5 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |       236        236   51.92118   366.4162     19.142   9.766903   142.4215    12253.4 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |       236        236       1581   339742.1    582.874   297.4022   4336.736     373116 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |       168        168   51.40357   391.7442   19.79253     11.984   141.5067     8635.8 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |       168        168   1565.239   363226.2   602.6825   364.9129   4308.878   262960.1 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |       142        142   57.79427   702.9112   26.51247   13.44876   161.6323   8206.787 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |       142        142   1759.836     651741   807.3048   409.5148   4921.704   249896.7 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |       105        105   53.47708   508.9081   22.55899   15.40541   131.7868   5615.094 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |       105        105   1628.377   471860.9   686.9213   469.0946   4012.908   170979.6 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |        NA         NA   55.46812   801.5442   28.31156   13.14256   194.0437   5158.535 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   1689.004   743193.9   862.0869   400.1909   5908.632   157077.4 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |        NA         NA   49.22208   269.0271   16.40205   17.42393   98.31537   3494.767 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   1498.812   249442.6   499.4423   530.5586   2993.703   106415.7 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |        NA         NA    50.2134   302.6932   17.39808   15.96562    95.0111   3314.084 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   1528.998   280657.9   529.7716    486.153   2893.088   100913.9 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |        NA         NA   51.30973   607.2892   24.64324       10.5     167.22   3027.274 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   1562.381   563080.1   750.3866    319.725   5091.848    92180.5 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |        NA         NA   50.72591   328.5719   18.12655   13.95691   101.5158   3246.458 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   1544.604   304652.7   551.9535   424.9878   3091.156   98854.65 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |        NA         NA   53.37515   280.6106   16.75144   13.07999    84.4676   2615.382 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   1625.273   260182.8   510.0812   398.2858   2572.038   79638.39 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |        NA         NA   51.23217   389.7041   19.74093   11.98675   120.4462   2459.144 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA    1560.02   361334.7   601.1112   364.9966   3667.586   74880.94 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |        NA         NA   64.92995   1124.659   33.53594   18.17999    192.239   2337.478 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   1977.117    1042787   1021.169   553.5808   5853.678   71176.22 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |        NA         NA   61.51315   1678.589   40.97059   10.17999   273.7207   2706.579 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   1873.076    1556392   1247.554   309.9808   8334.796   82415.32 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |        NA         NA   66.72052   10410.25   102.0306   12.60707   667.2952    2535.38 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA    2031.64    9652410   3106.833   383.8853   20319.14   77202.32 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~g |       300        300   61.67155   881.4463   29.68916   11.46386   382.6323   18501.47 
file results/two/5_sample2_wages_distribution.xlsx saved

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)     e(min)     e(max)     e(sum) 
-------------+----------------------------------------------------------------------------------------
wnc_offer_~y |       300        300   1877.899   817279.2    904.035   349.0745   11651.15   563369.6 
file results/two/5_sample2_wages_distribution.xlsx saved
r; t=57.04 15:33:21

.         
.         *full sample - normal distribution paramaters
.         capture noisily estpost sum wnc_offer_beg_monthly, d

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wnc_offer_~y |     15390      15390   1600.073   313820.2   560.1966   4.447437   99.25689   2.46e+07   257.8651   20319.14   576.5681   884.0388   1080.052   1289.792   1518.592    1817.29 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wnc_offer_~y |  2217.924   2515.779   3347.268 
r; t=0.83 15:33:22

.         if _rc!=2000{
.                 putexcel set results/${samplefolder}/5_sample${sample}_wages_distribution, sheet("wnc_offer_normal") modify
r; t=0.01 15:33:22
.                 #delimit ; 
delimiter now ;
.                 putexcel A2=("wnc_offer_beg_monthly_all") B1=("mean") B2=matrix(e(mean)) C1=("sd") C2=matrix(e(sd)) D1=("sum_w") D2=matrix(e(sum_w)) E1=("skewness") E2=matrix(e(skewness)) 
>                 F1=("kurtosis") F2=matrix(e(kurtosis)) G1=("sum") G2=matrix(e(sum)) H1=("min") H2=matrix(e(min)) I1=("max") I2=matrix(e(max)) J1=("p1") J2=matrix(e(p1)) 
>                 K1=("p5") K2=matrix(e(p5)) L1=("p10") L2=matrix(e(p10)) M1=("p25") M2=matrix(e(p25)) N1=("p50") N2=matrix(e(p50)) O1=("p75") O2=matrix(e(p75)) P1=("p90") P2=matrix(e(p90)) 
>                 Q1=("p95") Q2=matrix(e(p95)) R1=("p99") R2=matrix(e(p99));
file results/two/5_sample2_wages_distribution.xlsx saved
r; t=0.10 15:33:22
.                 #delimit cr
delimiter now cr
.                 `putexcelclose'
r; t=0.00 15:33:22
.         }
r; t=0.12 15:33:22

.         _pctile wnc_offer_beg_monthly, nq(1000) 
r; t=0.12 15:33:22

.         return list

scalars:
                 r(r1) =  319.7250061035156
                 r(r2) =  349.0745239257813
                 r(r3) =  381.5813903808594
                 r(r4) =  420.3865661621094
                 r(r5) =  464.6668395996094
                 r(r6) =  502.8443908691406
                 r(r7) =  527.4063110351563
                 r(r8) =  542.6456909179688
                 r(r9) =  556.6201782226563
                r(r10) =  576.5680541992188
                r(r11) =  590.47412109375
                r(r12) =  605.7911987304688
                r(r13) =  622.7535400390625
                r(r14) =  631.8433227539063
                r(r15) =  649.3250732421875
                r(r16) =  657.0963745117188
                r(r17) =  665.96630859375
                r(r18) =  676.0174560546875
                r(r19) =  682.28076171875
                r(r20) =  687.5086669921875
               r(r999) =  5500.78125
               r(r998) =  4714.166015625
               r(r997) =  4300.2861328125
               r(r996) =  4058.96337890625
               r(r995) =  3904.292236328125
               r(r994) =  3752.071533203125
               r(r993) =  3607.663330078125
               r(r992) =  3520.707763671875
               r(r991) =  3435.44580078125
               r(r990) =  3347.26806640625
               r(r989) =  3290.25390625
               r(r988) =  3256.9365234375
               r(r987) =  3184.016357421875
               r(r986) =  3131.47216796875
               r(r985) =  3089.53369140625
               r(r984) =  3060.8603515625
               r(r983) =  3034.08447265625
               r(r982) =  3004.419677734375
               r(r981) =  2978.270751953125
               r(r980) =  2949.640869140625
               r(r979) =  2930.81689453125
               r(r978) =  2911.525146484375
               r(r977) =  2895.000732421875
               r(r976) =  2874.334716796875
               r(r975) =  2846.27197265625
               r(r974) =  2829.44921875
               r(r973) =  2804.075927734375
               r(r972) =  2786.053955078125
               r(r971) =  2767.052734375
               r(r970) =  2757.121337890625
               r(r969) =  2743.208740234375
               r(r968) =  2725.132568359375
               r(r967) =  2709.298828125
               r(r966) =  2692.844482421875
               r(r965) =  2682.240478515625
               r(r964) =  2669.869384765625
               r(r963) =  2660.625732421875
               r(r962) =  2649.3271484375
               r(r961) =  2637.923828125
               r(r960) =  2618.730224609375
               r(r959) =  2610.151123046875
               r(r958) =  2593.278564453125
               r(r957) =  2584.7080078125
               r(r956) =  2573.795654296875
               r(r955) =  2563.445068359375
               r(r954) =  2554.5126953125
               r(r953) =  2546.947021484375
               r(r952) =  2537.815673828125
               r(r951) =  2526.12646484375
               r(r950) =  2515.77880859375
               r(r949) =  2503.459716796875
               r(r948) =  2496.749267578125
               r(r947) =  2489.19677734375
               r(r946) =  2482.78564453125
               r(r945) =  2477.6201171875
               r(r944) =  2469.284912109375
               r(r943) =  2460.294189453125
               r(r942) =  2451.36572265625
               r(r941) =  2444.984130859375
               r(r940) =  2440.82373046875
               r(r939) =  2433.421630859375
               r(r938) =  2427.439208984375
               r(r937) =  2420.072509765625
               r(r936) =  2415.432861328125
               r(r935) =  2408.424560546875
               r(r934) =  2402.76953125
               r(r933) =  2394.490478515625
               r(r932) =  2390.0419921875
               r(r931) =  2382.172607421875
               r(r930) =  2373.90283203125
               r(r929) =  2368.52392578125
               r(r928) =  2361.240966796875
               r(r927) =  2357.010498046875
               r(r926) =  2350.67724609375
               r(r925) =  2346.431640625
               r(r924) =  2341.51416015625
               r(r923) =  2335.6943359375
               r(r922) =  2329.729736328125
               r(r921) =  2324.3896484375
               r(r920) =  2316.951904296875
               r(r919) =  2310.903076171875
               r(r918) =  2303.621826171875
               r(r917) =  2297.7666015625
               r(r916) =  2293.727783203125
               r(r915) =  2289.08251953125
               r(r914) =  2284.43896484375
               r(r913) =  2280.8037109375
               r(r912) =  2273.325439453125
               r(r911) =  2269.53076171875
               r(r910) =  2263.286865234375
               r(r909) =  2258.816650390625
               r(r908) =  2255.1591796875
               r(r907) =  2250.31298828125
               r(r906) =  2246.3603515625
               r(r905) =  2242.235107421875
               r(r904) =  2235.14111328125
               r(r903) =  2230.34765625
               r(r902) =  2226.677978515625
               r(r901) =  2221.99169921875
               r(r900) =  2217.924438476563
               r(r899) =  2213.507080078125
               r(r898) =  2209.033935546875
               r(r897) =  2204.71142578125
               r(r896) =  2200.13720703125
               r(r895) =  2194.5283203125
               r(r894) =  2191.052490234375
               r(r893) =  2187.45947265625
               r(r892) =  2182.789794921875
               r(r891) =  2180.018310546875
               r(r890) =  2176.3935546875
               r(r889) =  2171.43310546875
               r(r888) =  2167.03369140625
               r(r887) =  2163.419189453125
               r(r886) =  2159.24169921875
               r(r885) =  2154.383544921875
               r(r884) =  2150.76025390625
               r(r883) =  2144.905029296875
               r(r882) =  2139.655029296875
               r(r881) =  2135.75732421875
               r(r880) =  2131.286865234375
               r(r879) =  2126.052978515625
               r(r878) =  2122.099365234375
               r(r877) =  2118.60888671875
               r(r876) =  2114.202392578125
               r(r875) =  2111.946533203125
               r(r874) =  2106.845458984375
               r(r873) =  2103.466064453125
               r(r872) =  2099.135498046875
               r(r871) =  2096.643798828125
               r(r870) =  2092.799072265625
               r(r869) =  2090.671630859375
               r(r868) =  2088.3515625
               r(r867) =  2084.93115234375
               r(r866) =  2081.49853515625
               r(r865) =  2078.100830078125
               r(r864) =  2075.284423828125
               r(r863) =  2071.525634765625
               r(r862) =  2068.23388671875
               r(r861) =  2064.36962890625
               r(r860) =  2062.3154296875
               r(r859) =  2059.28662109375
               r(r858) =  2056.69091796875
               r(r857) =  2053.904296875
               r(r856) =  2051.37353515625
               r(r855) =  2048.123046875
               r(r854) =  2046.2392578125
               r(r853) =  2042.929931640625
               r(r852) =  2039.694702148438
               r(r851) =  2036.486206054688
               r(r850) =  2034.247192382813
               r(r849) =  2031.729125976563
               r(r848) =  2029.671997070313
               r(r847) =  2027.78466796875
               r(r846) =  2024.335205078125
               r(r845) =  2020.98291015625
               r(r844) =  2018.294311523438
               r(r843) =  2015.341064453125
               r(r842) =  2013.525634765625
               r(r841) =  2011.165283203125
               r(r840) =  2008.68505859375
               r(r839) =  2005.840576171875
               r(r838) =  2004.118408203125
               r(r837) =  2002.297607421875
               r(r836) =  2000.322631835938
               r(r835) =  1997.697509765625
               r(r834) =  1994.977294921875
               r(r833) =  1993.243286132813
               r(r832) =  1989.822265625
               r(r831) =  1988.169067382813
               r(r830) =  1985.567138671875
               r(r829) =  1982.957153320313
               r(r828) =  1980.960815429688
               r(r827) =  1978.716674804688
               r(r826) =  1975.29541015625
               r(r825) =  1973.5283203125
               r(r824) =  1971.64794921875
               r(r823) =  1967.811767578125
               r(r822) =  1965.884765625
               r(r821) =  1964.039428710938
               r(r820) =  1961.551635742188
               r(r819) =  1958.926391601563
               r(r818) =  1956.10009765625
               r(r817) =  1954.07568359375
               r(r816) =  1951.253784179688
               r(r815) =  1950.472534179688
               r(r814) =  1948.564819335938
               r(r813) =  1945.427734375
               r(r812) =  1942.861206054688
               r(r811) =  1940.069946289063
               r(r810) =  1937.925903320313
               r(r809) =  1935.377807617188
               r(r808) =  1933.37060546875
               r(r807) =  1931.22265625
               r(r806) =  1930.148681640625
               r(r805) =  1928.095336914063
               r(r804) =  1926.00732421875
               r(r803) =  1922.100830078125
               r(r802) =  1919.52294921875
               r(r801) =  1917.3291015625
               r(r800) =  1915.648254394531
               r(r799) =  1912.1865234375
               r(r798) =  1909.067626953125
               r(r797) =  1907.372802734375
               r(r796) =  1906.036987304688
               r(r795) =  1903.845336914063
               r(r794) =  1900.972534179688
               r(r793) =  1898.953735351563
               r(r792) =  1897.26025390625
               r(r791) =  1894.949829101563
               r(r790) =  1891.2890625
               r(r789) =  1889.3408203125
               r(r788) =  1887.599975585938
               r(r787) =  1885.13671875
               r(r786) =  1883.332763671875
               r(r785) =  1881.17138671875
               r(r784) =  1879.28271484375
               r(r783) =  1877.144287109375
               r(r782) =  1874.747314453125
               r(r781) =  1872.299194335938
               r(r780) =  1870.66943359375
               r(r779) =  1868.830322265625
               r(r778) =  1867.29248046875
               r(r777) =  1865.397827148438
               r(r776) =  1863.407348632813
               r(r775) =  1861.298217773438
               r(r774) =  1858.163818359375
               r(r773) =  1856.65625
               r(r772) =  1855.740112304688
               r(r771) =  1853.90478515625
               r(r770) =  1852.014526367188
               r(r769) =  1850.086181640625
               r(r768) =  1848.18359375
               r(r767) =  1845.846069335938
               r(r766) =  1843.657104492188
               r(r765) =  1842.279052734375
               r(r764) =  1839.989624023438
               r(r763) =  1838.863037109375
               r(r762) =  1836.96435546875
               r(r761) =  1834.942016601563
               r(r760) =  1833.641723632813
               r(r759) =  1832.28759765625
               r(r758) =  1830.863647460938
               r(r757) =  1829.485595703125
               r(r756) =  1827.517822265625
               r(r755) =  1825.915283203125
               r(r754) =  1824.476684570313
               r(r753) =  1822.088134765625
               r(r752) =  1820.16845703125
               r(r751) =  1818.587768554688
               r(r750) =  1817.290161132813
               r(r749) =  1815.98193359375
               r(r748) =  1815.075073242188
               r(r747) =  1813.739868164063
               r(r746) =  1812.3251953125
               r(r745) =  1811.021240234375
               r(r744) =  1810.239501953125
               r(r743) =  1807.77490234375
               r(r742) =  1805.597778320313
               r(r741) =  1804.53466796875
               r(r740) =  1803.238647460938
               r(r739) =  1801.7294921875
               r(r738) =  1800.337036132813
               r(r737) =  1798.78125
               r(r736) =  1797.382202148438
               r(r735) =  1795.73486328125
               r(r734) =  1794.253662109375
               r(r733) =  1792.73828125
               r(r732) =  1791.286010742188
               r(r731) =  1789.507080078125
               r(r730) =  1788.270874023438
               r(r729) =  1786.478271484375
               r(r728) =  1784.66064453125
               r(r727) =  1783.743286132813
               r(r726) =  1781.756469726563
               r(r725) =  1779.492309570313
               r(r724) =  1778.368530273438
               r(r723) =  1776.987915039063
               r(r722) =  1775.751831054688
               r(r721) =  1774.509765625
               r(r720) =  1773.435180664063
               r(r719) =  1772.3427734375
               r(r718) =  1771.131469726563
               r(r717) =  1769.237548828125
               r(r716) =  1768.077514648438
               r(r715) =  1766.948608398438
               r(r714) =  1765.81884765625
               r(r713) =  1764.653076171875
               r(r712) =  1763.351928710938
               r(r711) =  1761.695068359375
               r(r710) =  1759.973876953125
               r(r709) =  1758.825439453125
               r(r708) =  1758.043212890625
               r(r707) =  1755.381103515625
               r(r706) =  1753.959716796875
               r(r705) =  1751.936401367188
               r(r704) =  1750.543823242188
               r(r703) =  1749.5107421875
               r(r702) =  1747.016357421875
               r(r701) =  1745.270385742188
               r(r700) =  1743.861694335938
               r(r699) =  1742.457153320313
               r(r698) =  1741.762939453125
               r(r697) =  1740.034545898438
               r(r696) =  1738.487915039063
               r(r695) =  1736.955322265625
               r(r694) =  1735.392944335938
               r(r693) =  1734.322021484375
               r(r692) =  1733.041015625
               r(r691) =  1731.956787109375
               r(r690) =  1730.364624023438
               r(r689) =  1728.664306640625
               r(r688) =  1726.909057617188
               r(r687) =  1725.379028320313
               r(r686) =  1723.987060546875
               r(r685) =  1722.806274414063
               r(r684) =  1721.255249023438
               r(r683) =  1720.24755859375
               r(r682) =  1718.687866210938
               r(r681) =  1717.151123046875
               r(r680) =  1715.433837890625
               r(r679) =  1714.388793945313
               r(r678) =  1712.773803710938
               r(r677) =  1711.763793945313
               r(r676) =  1710.443725585938
               r(r675) =  1708.703002929688
               r(r674) =  1707.523681640625
               r(r673) =  1706.188720703125
               r(r672) =  1704.69921875
               r(r671) =  1703.123901367188
               r(r670) =  1701.870727539063
               r(r669) =  1700.595581054688
               r(r668) =  1698.845947265625
               r(r667) =  1698.377807617188
               r(r666) =  1696.544555664063
               r(r665) =  1695.366088867188
               r(r664) =  1693.425048828125
               r(r663) =  1692.457641601563
               r(r662) =  1691.490356445313
               r(r661) =  1690.76318359375
               r(r660) =  1689.86669921875
               r(r659) =  1688.945678710938
               r(r658) =  1687.677612304688
               r(r657) =  1686.787231445313
               r(r656) =  1685.8603515625
               r(r655) =  1684.915161132813
               r(r654) =  1684.141723632813
               r(r653) =  1683.03759765625
               r(r652) =  1681.882934570313
               r(r651) =  1680.489135742188
               r(r650) =  1678.914916992188
               r(r649) =  1677.839111328125
               r(r648) =  1676.627807617188
               r(r647) =  1675.569091796875
               r(r646) =  1674.387329101563
               r(r645) =  1673.598999023438
               r(r644) =  1672.41943359375
               r(r643) =  1671.242919921875
               r(r642) =  1669.907592773438
               r(r641) =  1668.862548828125
               r(r640) =  1667.5439453125
               r(r639) =  1666.348388671875
               r(r638) =  1665.099731445313
               r(r637) =  1664.108764648438
               r(r636) =  1662.896240234375
               r(r635) =  1662.011596679688
               r(r634) =  1661.28125
               r(r633) =  1660.271118164063
               r(r632) =  1659.035034179688
               r(r631) =  1657.848510742188
               r(r630) =  1657.041015625
               r(r629) =  1655.886596679688
               r(r628) =  1654.9375
               r(r627) =  1653.668701171875
               r(r626) =  1652.7060546875
               r(r625) =  1651.517578125
               r(r624) =  1650.055053710938
               r(r623) =  1648.651245117188
               r(r622) =  1647.422241210938
               r(r621) =  1646.218872070313
               r(r620) =  1645.393920898438
               r(r619) =  1644.076416015625
               r(r618) =  1642.55322265625
               r(r617) =  1641.290649414063
               r(r616) =  1640.239013671875
               r(r615) =  1638.844360351563
               r(r614) =  1637.398803710938
               r(r613) =  1636.598388671875
               r(r612) =  1635.299926757813
               r(r611) =  1633.61669921875
               r(r610) =  1632.309814453125
               r(r609) =  1631.149658203125
               r(r608) =  1630.093994140625
               r(r607) =  1629.117797851563
               r(r606) =  1628.366577148438
               r(r605) =  1626.952758789063
               r(r604) =  1625.76025390625
               r(r603) =  1624.59130859375
               r(r602) =  1623.31884765625
               r(r601) =  1622.060180664063
               r(r600) =  1620.727172851563
               r(r599) =  1619.158081054688
               r(r598) =  1618.19189453125
               r(r597) =  1617.308837890625
               r(r596) =  1616.856323242188
               r(r595) =  1615.740478515625
               r(r594) =  1614.250122070313
               r(r593) =  1613.3720703125
               r(r592) =  1612.390014648438
               r(r591) =  1611.009643554688
               r(r590) =  1610.026245117188
               r(r589) =  1609.3349609375
               r(r588) =  1608.577270507813
               r(r587) =  1607.167724609375
               r(r586) =  1605.952270507813
               r(r585) =  1604.515380859375
               r(r584) =  1603.818969726563
               r(r583) =  1602.871826171875
               r(r582) =  1601.308227539063
               r(r581) =  1600.381225585938
               r(r580) =  1599.048583984375
               r(r579) =  1598.019897460938
               r(r578) =  1596.917236328125
               r(r577) =  1595.805419921875
               r(r576) =  1594.64453125
               r(r575) =  1593.324951171875
               r(r574) =  1592.535522460938
               r(r573) =  1591.68896484375
               r(r572) =  1590.534545898438
               r(r571) =  1588.182983398438
               r(r570) =  1587.246948242188
               r(r569) =  1585.78515625
               r(r568) =  1584.9267578125
               r(r567) =  1584.144165039063
               r(r566) =  1583.061279296875
               r(r565) =  1582.076782226563
               r(r564) =  1581.11474609375
               r(r563) =  1580.292114257813
               r(r562) =  1579.09521484375
               r(r561) =  1578.287719726563
               r(r560) =  1577.277709960938
               r(r559) =  1576.47021484375
               r(r558) =  1575.449462890625
               r(r557) =  1574.596923828125
               r(r556) =  1573.536987304688
               r(r555) =  1572.742431640625
               r(r554) =  1571.644653320313
               r(r553) =  1570.783935546875
               r(r552) =  1569.806274414063
               r(r551) =  1568.487548828125
               r(r550) =  1567.5849609375
               r(r549) =  1566.834594726563
               r(r548) =  1566.133666992188
               r(r547) =  1565.162353515625
               r(r546) =  1563.561279296875
               r(r545) =  1562.73974609375
               r(r544) =  1561.803466796875
               r(r543) =  1560.789794921875
               r(r542) =  1559.835571289063
               r(r541) =  1558.90234375
               r(r540) =  1558.064086914063
               r(r539) =  1557.073486328125
               r(r538) =  1555.702758789063
               r(r537) =  1555.357421875
               r(r536) =  1554.474365234375
               r(r535) =  1553.652099609375
               r(r534) =  1552.04833984375
               r(r533) =  1551.080810546875
               r(r532) =  1549.815795898438
               r(r531) =  1549.008178710938
               r(r530) =  1548.20068359375
               r(r529) =  1547.435668945313
               r(r528) =  1545.655883789063
               r(r527) =  1544.46484375
               r(r526) =  1543.4990234375
               r(r525) =  1542.120971679688
               r(r524) =  1541.5
               r(r523) =  1540.474365234375
               r(r522) =  1539.329833984375
               r(r521) =  1537.994018554688
               r(r520) =  1537.041015625
               r(r519) =  1535.995727539063
               r(r518) =  1535.23193359375
               r(r517) =  1534.09912109375
               r(r516) =  1533.606323242188
               r(r515) =  1532.6220703125
               r(r514) =  1531.69873046875
               r(r513) =  1531.036499023438
               r(r512) =  1530.026489257813
               r(r511) =  1528.814086914063
               r(r510) =  1527.872436523438
               r(r509) =  1526.73828125
               r(r508) =  1526.355590820313
               r(r507) =  1525.390747070313
               r(r506) =  1524.210083007813
               r(r505) =  1523.493774414063
               r(r504) =  1522.457275390625
               r(r503) =  1521.516357421875
               r(r502) =  1520.5361328125
               r(r501) =  1519.32373046875
               r(r500) =  1518.592468261719
               r(r499) =  1517.714721679688
               r(r498) =  1516.81591796875
               r(r497) =  1516.052856445313
               r(r496) =  1515.301391601563
               r(r495) =  1514.477416992188
               r(r494) =  1513.636474609375
               r(r493) =  1512.597290039063
               r(r492) =  1511.821044921875
               r(r491) =  1510.710693359375
               r(r490) =  1510.050415039063
               r(r489) =  1509.374877929688
               r(r488) =  1508.23046875
               r(r487) =  1507.258178710938
               r(r486) =  1506.51318359375
               r(r485) =  1505.202514648438
               r(r484) =  1504.47119140625
               r(r483) =  1503.346923828125
               r(r482) =  1502.73046875
               r(r481) =  1501.465576171875
               r(r480) =  1500.746948242188
               r(r479) =  1499.534423828125
               r(r478) =  1498.475463867188
               r(r477) =  1497.546264648438
               r(r476) =  1496.591918945313
               r(r475) =  1495.256103515625
               r(r474) =  1494.111572265625
               r(r473) =  1493.172607421875
               r(r472) =  1492.198364257813
               r(r471) =  1491.126831054688
               r(r470) =  1490.447998046875
               r(r469) =  1489.376220703125
               r(r468) =  1488.58447265625
               r(r467) =  1487.621704101563
               r(r466) =  1486.871826171875
               r(r465) =  1486.207763671875
               r(r464) =  1485.711303710938
               r(r463) =  1484.591552734375
               r(r462) =  1484.163696289063
               r(r461) =  1483.380249023438
               r(r460) =  1482.473022460938
               r(r459) =  1481.262817382813
               r(r458) =  1480.09375
               r(r457) =  1479.14013671875
               r(r456) =  1477.726318359375
               r(r455) =  1476.621215820313
               r(r454) =  1475.70751953125
               r(r453) =  1474.820434570313
               r(r452) =  1473.6259765625
               r(r451) =  1472.2490234375
               r(r450) =  1471.264892578125
               r(r449) =  1470.457397460938
               r(r448) =  1469.30859375
               r(r447) =  1468.639892578125
               r(r446) =  1467.918823242188
               r(r445) =  1465.98486328125
               r(r444) =  1465.004760742188
               r(r443) =  1463.994750976563
               r(r442) =  1463.3896484375
               r(r441) =  1462.37353515625
               r(r440) =  1461.572143554688
               r(r439) =  1460.43896484375
               r(r438) =  1459.408447265625
               r(r437) =  1458.624267578125
               r(r436) =  1457.724975585938
               r(r435) =  1456.927001953125
               r(r434) =  1456.120849609375
               r(r433) =  1455.11083984375
               r(r432) =  1454.337036132813
               r(r431) =  1453.5498046875
               r(r430) =  1452.485595703125
               r(r429) =  1451.865966796875
               r(r428) =  1450.869384765625
               r(r427) =  1450.061889648438
               r(r426) =  1449.25439453125
               r(r425) =  1448.265869140625
               r(r424) =  1447.93896484375
               r(r423) =  1446.98583984375
               r(r422) =  1446.071166992188
               r(r421) =  1445.284057617188
               r(r420) =  1444.205444335938
               r(r419) =  1443.315063476563
               r(r418) =  1442.79296875
               r(r417) =  1441.833862304688
               r(r416) =  1440.975341796875
               r(r415) =  1439.875610351563
               r(r414) =  1438.908203125
               r(r413) =  1437.804077148438
               r(r412) =  1436.532836914063
               r(r411) =  1435.639038085938
               r(r410) =  1435.039916992188
               r(r409) =  1434.260986328125
               r(r408) =  1433.493041992188
               r(r407) =  1432.292724609375
               r(r406) =  1431.361694335938
               r(r405) =  1430.0712890625
               r(r404) =  1429.05078125
               r(r403) =  1428.553833007813
               r(r402) =  1427.766723632813
               r(r401) =  1426.9521484375
               r(r400) =  1425.997802734375
               r(r399) =  1424.960327148438
               r(r398) =  1424.090209960938
               r(r397) =  1423.135986328125
               r(r396) =  1422.75439453125
               r(r395) =  1421.888305664063
               r(r394) =  1421.073974609375
               r(r393) =  1419.719848632813
               r(r392) =  1418.964721679688
               r(r391) =  1418.56103515625
               r(r390) =  1418.021240234375
               r(r389) =  1416.743774414063
               r(r388) =  1415.886352539063
               r(r387) =  1415.31298828125
               r(r386) =  1414.38232421875
               r(r385) =  1413.51220703125
               r(r384) =  1412.606079101563
               r(r383) =  1412.019775390625
               r(r382) =  1411.089599609375
               r(r381) =  1410.079467773438
               r(r380) =  1409.461059570313
               r(r379) =  1408.635620117188
               r(r378) =  1407.491088867188
               r(r377) =  1406.803100585938
               r(r376) =  1405.8369140625
               r(r375) =  1405.233154296875
               r(r374) =  1404.934692382813
               r(r373) =  1404.09619140625
               r(r372) =  1403.21435546875
               r(r371) =  1402.204345703125
               r(r370) =  1401.388061523438
               r(r369) =  1400.227905273438
               r(r368) =  1399.454467773438
               r(r367) =  1398.90576171875
               r(r366) =  1398.014038085938
               r(r365) =  1397.487915039063
               r(r364) =  1396.550415039063
               r(r363) =  1396.166870117188
               r(r362) =  1395.005493164063
               r(r361) =  1394.083740234375
               r(r360) =  1392.714111328125
               r(r359) =  1391.912231445313
               r(r358) =  1390.944580078125
               r(r357) =  1390.172241210938
               r(r356) =  1389.482788085938
               r(r355) =  1388.79541015625
               r(r354) =  1388.069091796875
               r(r353) =  1387.293823242188
               r(r352) =  1386.039306640625
               r(r351) =  1385.242553710938
               r(r350) =  1384.232666015625
               r(r349) =  1383.069580078125
               r(r348) =  1382.241821289063
               r(r347) =  1381.607543945313
               r(r346) =  1380.731201171875
               r(r345) =  1378.954223632813
               r(r344) =  1378.179565429688
               r(r343) =  1377.771240234375
               r(r342) =  1376.76123046875
               r(r341) =  1375.549926757813
               r(r340) =  1374.690551757813
               r(r339) =  1374.118530273438
               r(r338) =  1373.529907226563
               r(r337) =  1372.377807617188
               r(r336) =  1371.217651367188
               r(r335) =  1370.249267578125
               r(r334) =  1369.693481445313
               r(r333) =  1368.57470703125
               r(r332) =  1368.122680664063
               r(r331) =  1367.230224609375
               r(r330) =  1365.51611328125
               r(r329) =  1364.84716796875
               r(r328) =  1363.481079101563
               r(r327) =  1362.617431640625
               r(r326) =  1361.633056640625
               r(r325) =  1360.648559570313
               r(r324) =  1359.420043945313
               r(r323) =  1358.580932617188
               r(r322) =  1357.486572265625
               r(r321) =  1356.515625
               r(r320) =  1355.723876953125
               r(r319) =  1354.7783203125
               r(r318) =  1353.939819335938
               r(r317) =  1352.932983398438
               r(r316) =  1352.0517578125
               r(r315) =  1351.045166015625
               r(r314) =  1350.413696289063
               r(r313) =  1349.702880859375
               r(r312) =  1348.535400390625
               r(r311) =  1348.052490234375
               r(r310) =  1346.871704101563
               r(r309) =  1345.922729492188
               r(r308) =  1345.057861328125
               r(r307) =  1343.766235351563
               r(r306) =  1342.737670898438
               r(r305) =  1341.433959960938
               r(r304) =  1340.572509765625
               r(r303) =  1339.20263671875
               r(r302) =  1338.192626953125
               r(r301) =  1337.424072265625
               r(r300) =  1336.6357421875
               r(r299) =  1335.942626953125
               r(r298) =  1335.365112304688
               r(r297) =  1334.356323242188
               r(r296) =  1334.077026367188
               r(r295) =  1333.2900390625
               r(r294) =  1332.617919921875
               r(r293) =  1332.247192382813
               r(r292) =  1331.327514648438
               r(r291) =  1330.523071289063
               r(r290) =  1330.023071289063
               r(r289) =  1329.71337890625
               r(r288) =  1329.249633789063
               r(r287) =  1327.983764648438
               r(r286) =  1327.0791015625
               r(r285) =  1326.154907226563
               r(r284) =  1325.2685546875
               r(r283) =  1323.9130859375
               r(r282) =  1323.04736328125
               r(r281) =  1321.821655273438
               r(r280) =  1320.75537109375
               r(r279) =  1319.31591796875
               r(r278) =  1318.403442382813
               r(r277) =  1317.348022460938
               r(r276) =  1316.788330078125
               r(r275) =  1315.576416015625
               r(r274) =  1314.592041015625
               r(r273) =  1313.527099609375
               r(r272) =  1312.230590820313
               r(r271) =  1311.375
               r(r270) =  1310.325805664063
               r(r269) =  1309.720703125
               r(r268) =  1308.306884765625
               r(r267) =  1307.703002929688
               r(r266) =  1306.855224609375
               r(r265) =  1305.5634765625
               r(r264) =  1304.874267578125
               r(r263) =  1303.317138671875
               r(r262) =  1302.192016601563
               r(r261) =  1301.109375
               r(r260) =  1300.073974609375
               r(r259) =  1299.421752929688
               r(r258) =  1298.69140625
               r(r257) =  1297.40234375
               r(r256) =  1296.483764648438
               r(r255) =  1295.684936523438
               r(r254) =  1294.3740234375
               r(r253) =  1293.6630859375
               r(r252) =  1292.35400390625
               r(r251) =  1291.170166015625
               r(r250) =  1289.792114257813
               r(r249) =  1288.92138671875
               r(r248) =  1288.218017578125
               r(r247) =  1287.3701171875
               r(r246) =  1285.85546875
               r(r245) =  1285.154296875
               r(r244) =  1284.277587890625
               r(r243) =  1283.469970703125
               r(r242) =  1282.509643554688
               r(r241) =  1280.873657226563
               r(r240) =  1280.147094726563
               r(r239) =  1279.632446289063
               r(r238) =  1278.62353515625
               r(r237) =  1277.924194335938
               r(r236) =  1277.36962890625
               r(r235) =  1276.7568359375
               r(r234) =  1275.392333984375
               r(r233) =  1274.242553710938
               r(r232) =  1273.574829101563
               r(r231) =  1272.776123046875
               r(r230) =  1271.651000976563
               r(r229) =  1270.261840820313
               r(r228) =  1269.546142578125
               r(r227) =  1268.449096679688
               r(r226) =  1267.026245117188
               r(r225) =  1265.758911132813
               r(r224) =  1264.285766601563
               r(r223) =  1263.098754882813
               r(r222) =  1262.138671875
               r(r221) =  1261.260620117188
               r(r220) =  1260.247436523438
               r(r219) =  1259.592407226563
               r(r218) =  1257.90869140625
               r(r217) =  1256.197143554688
               r(r216) =  1254.596069335938
               r(r215) =  1253.138305664063
               r(r214) =  1251.968505859375
               r(r213) =  1251.252319335938
               r(r212) =  1250.341064453125
               r(r211) =  1249.24658203125
               r(r210) =  1247.868530273438
               r(r209) =  1246.490478515625
               r(r208) =  1244.945190429688
               r(r207) =  1243.396850585938
               r(r206) =  1242.16015625
               r(r205) =  1240.862670898438
               r(r204) =  1238.223510742188
               r(r203) =  1236.278686523438
               r(r202) =  1235.312133789063
               r(r201) =  1234.015625
               r(r200) =  1232.924926757813
               r(r199) =  1232.052856445313
               r(r198) =  1230.547973632813
               r(r197) =  1229.28955078125
               r(r196) =  1228.186157226563
               r(r195) =  1227.713256835938
               r(r194) =  1225.927490234375
               r(r193) =  1225.23388671875
               r(r192) =  1224.643432617188
               r(r191) =  1223.462280273438
               r(r190) =  1221.880859375
               r(r189) =  1220.313232421875
               r(r188) =  1219.255859375
               r(r187) =  1218.3447265625
               r(r186) =  1216.934204101563
               r(r185) =  1215.851928710938
               r(r184) =  1214.739013671875
               r(r183) =  1212.793701171875
               r(r182) =  1211.582397460938
               r(r181) =  1210.471923828125
               r(r180) =  1209.147094726563
               r(r179) =  1208.111328125
               r(r178) =  1206.93798828125
               r(r177) =  1205.74755859375
               r(r176) =  1204.17529296875
               r(r175) =  1203.77978515625
               r(r174) =  1202.20556640625
               r(r173) =  1200.669189453125
               r(r172) =  1198.81298828125
               r(r171) =  1198.072021484375
               r(r170) =  1197.235717773438
               r(r169) =  1195.83203125
               r(r168) =  1194.608764648438
               r(r167) =  1193.544921875
               r(r166) =  1192.514526367188
               r(r165) =  1190.78369140625
               r(r164) =  1189.171630859375
               r(r163) =  1187.88671875
               r(r162) =  1186.852783203125
               r(r161) =  1185.35009765625
               r(r160) =  1184.490844726563
               r(r159) =  1183.045043945313
               r(r158) =  1181.1357421875
               r(r157) =  1179.96435546875
               r(r156) =  1177.011474609375
               r(r155) =  1175.043823242188
               r(r154) =  1173.463745117188
               r(r153) =  1172.010986328125
               r(r152) =  1170.2734375
               r(r151) =  1168.411376953125
               r(r150) =  1167.359375
               r(r149) =  1165.791137695313
               r(r148) =  1164.414794921875
               r(r147) =  1163.627685546875
               r(r146) =  1161.503662109375
               r(r145) =  1160.110961914063
               r(r144) =  1158.3134765625
               r(r143) =  1156.933227539063
               r(r142) =  1156.25341796875
               r(r141) =  1154.307006835938
               r(r140) =  1153.162475585938
               r(r139) =  1152.41650390625
               r(r138) =  1151.382568359375
               r(r137) =  1149.324096679688
               r(r136) =  1148.078735351563
               r(r135) =  1147.258911132813
               r(r134) =  1145.52001953125
               r(r133) =  1144.195068359375
               r(r132) =  1142.92626953125
               r(r131) =  1141.780151367188
               r(r130) =  1140.351928710938
               r(r129) =  1138.007568359375
               r(r128) =  1137.843383789063
               r(r127) =  1135.074096679688
               r(r126) =  1133.74462890625
               r(r125) =  1132.67236328125
               r(r124) =  1130.611572265625
               r(r123) =  1129.121826171875
               r(r122) =  1127.258056640625
               r(r121) =  1125.963989257813
               r(r120) =  1123.970458984375
               r(r119) =  1122.191284179688
               r(r118) =  1120.463989257813
               r(r117) =  1119.300048828125
               r(r116) =  1116.876831054688
               r(r115) =  1115.208740234375
               r(r114) =  1112.615478515625
               r(r113) =  1110.51220703125
               r(r112) =  1108.597900390625
               r(r111) =  1106.608276367188
               r(r110) =  1105.081787109375
               r(r109) =  1102.792236328125
               r(r108) =  1100.484375
               r(r107) =  1098.613525390625
               r(r106) =  1096.007080078125
               r(r105) =  1092.967529296875
               r(r104) =  1091.195922851563
               r(r103) =  1088.046875
               r(r102) =  1086.385375976563
               r(r101) =  1082.276000976563
               r(r100) =  1080.051513671875
                r(r99) =  1077.655517578125
                r(r98) =  1075.699584960938
                r(r97) =  1072.24560546875
                r(r96) =  1069.151611328125
                r(r95) =  1067.230834960938
                r(r94) =  1062.759765625
                r(r93) =  1061.001342773438
                r(r92) =  1058.680786132813
                r(r91) =  1055.89404296875
                r(r90) =  1053.470825195313
                r(r89) =  1050.1708984375
                r(r88) =  1047.745361328125
                r(r87) =  1044.409423828125
                r(r86) =  1040.1435546875
                r(r85) =  1036.968505859375
                r(r84) =  1033.329711914063
                r(r83) =  1029.58984375
                r(r82) =  1026.244018554688
                r(r81) =  1023.786926269531
                r(r80) =  1020.354248046875
                r(r79) =  1016.2060546875
                r(r78) =  1012.882873535156
                r(r77) =  1010.120910644531
                r(r76) =  1006.547546386719
                r(r75) =  1001.503662109375
                r(r74) =  997.812744140625
                r(r73) =  994.5073852539063
                r(r72) =  990.603515625
                r(r71) =  986.2887573242188
                r(r70) =  982.095947265625
                r(r69) =  977.1691284179688
                r(r68) =  970.6792602539063
                r(r67) =  965.8186645507813
                r(r66) =  963.0315551757813
                r(r65) =  960.2686767578125
                r(r64) =  956.2111206054688
                r(r63) =  953.418701171875
                r(r62) =  948.3013305664063
                r(r61) =  942.5250244140625
                r(r60) =  937.4175415039063
                r(r59) =  932.7945556640625
                r(r58) =  926.6889038085938
                r(r57) =  921.7799072265625
                r(r56) =  917.515380859375
                r(r55) =  913.2666625976563
                r(r54) =  908.8679809570313
                r(r53) =  900.0790405273438
                r(r52) =  893.584228515625
                r(r51) =  888.1004028320313
                r(r50) =  884.0387573242188
                r(r49) =  878.2369995117188
                r(r48) =  872.3887939453125
                r(r47) =  867.0890502929688
                r(r46) =  862.5819091796875
                r(r45) =  855.1629638671875
                r(r44) =  849.8890380859375
                r(r43) =  842.5702514648438
                r(r42) =  836.1110229492188
                r(r41) =  829.7318115234375
                r(r40) =  825.244873046875
                r(r39) =  817.7451171875
                r(r38) =  810.1305541992188
                r(r37) =  804.7440185546875
                r(r36) =  799.3982543945313
                r(r35) =  791.5404663085938
                r(r34) =  787.2984619140625
                r(r33) =  779.1863403320313
                r(r32) =  775.2401733398438
                r(r31) =  768.9508666992188
                r(r30) =  759.536376953125
                r(r29) =  755.8255004882813
                r(r28) =  747.5028076171875
                r(r27) =  735.927734375
                r(r26) =  726.6768188476563
                r(r25) =  721.0702514648438
                r(r24) =  714.429443359375
                r(r23) =  709.1595458984375
                r(r22) =  703.2081909179688
                r(r21) =  695.3815307617188
r; t=0.12 15:33:23

.         
.         _pctile wnc_offer_beg_monthly if wnc_offer_beg_monthly<=$incmax, nq(1000)       
r; t=0.21 15:33:23

.         return list

scalars:
                 r(r1) =  319.7250061035156
                 r(r2) =  349.0745239257813
                 r(r3) =  381.5813903808594
                 r(r4) =  420.3865661621094
                 r(r5) =  464.6668395996094
                 r(r6) =  502.8443908691406
                 r(r7) =  527.4063110351563
                 r(r8) =  542.6456909179688
                 r(r9) =  556.6201782226563
                r(r10) =  576.5680541992188
                r(r11) =  590.47412109375
                r(r12) =  605.7911987304688
                r(r13) =  622.7535400390625
                r(r14) =  631.8433227539063
                r(r15) =  649.3250732421875
                r(r16) =  657.0963745117188
                r(r17) =  665.96630859375
                r(r18) =  675.6990966796875
                r(r19) =  682.28076171875
                r(r20) =  687.5086669921875
               r(r999) =  5321.83251953125
               r(r998) =  4496.77783203125
               r(r997) =  4247.84814453125
               r(r996) =  4012.908203125
               r(r995) =  3883.622314453125
               r(r994) =  3689.5234375
               r(r993) =  3578.36376953125
               r(r992) =  3502.298583984375
               r(r991) =  3405.9775390625
               r(r990) =  3340.93798828125
               r(r989) =  3278.931396484375
               r(r988) =  3247.6494140625
               r(r987) =  3171.08203125
               r(r986) =  3107.647705078125
               r(r985) =  3086.013427734375
               r(r984) =  3053.4765625
               r(r983) =  3027.33642578125
               r(r982) =  2996.25244140625
               r(r981) =  2975.377685546875
               r(r980) =  2944.10595703125
               r(r979) =  2928.2431640625
               r(r978) =  2909.2392578125
               r(r977) =  2891.6435546875
               r(r976) =  2869.83544921875
               r(r975) =  2840.93115234375
               r(r974) =  2823.6630859375
               r(r973) =  2801.798583984375
               r(r972) =  2783.408203125
               r(r971) =  2766.647705078125
               r(r970) =  2754.712646484375
               r(r969) =  2740.49951171875
               r(r968) =  2719.613525390625
               r(r967) =  2706.537109375
               r(r966) =  2689.630126953125
               r(r965) =  2679.081298828125
               r(r964) =  2668.978759765625
               r(r963) =  2658.499267578125
               r(r962) =  2646.0947265625
               r(r961) =  2633.912109375
               r(r960) =  2615.412109375
               r(r959) =  2605.508544921875
               r(r958) =  2589.9580078125
               r(r957) =  2582.5703125
               r(r956) =  2570.572509765625
               r(r955) =  2562.46044921875
               r(r954) =  2553.40771484375
               r(r953) =  2544.77880859375
               r(r952) =  2535.2919921875
               r(r951) =  2523.726318359375
               r(r950) =  2511.131103515625
               r(r949) =  2501.918212890625
               r(r948) =  2493.545166015625
               r(r947) =  2486.453369140625
               r(r946) =  2482.236572265625
               r(r945) =  2475.666259765625
               r(r944) =  2468.689453125
               r(r943) =  2458.315185546875
               r(r942) =  2450.22314453125
               r(r941) =  2444.419189453125
               r(r940) =  2437.9052734375
               r(r939) =  2431.37255859375
               r(r938) =  2426.258544921875
               r(r937) =  2418.702880859375
               r(r936) =  2413.19384765625
               r(r935) =  2407.03125
               r(r934) =  2401.261474609375
               r(r933) =  2392.765869140625
               r(r932) =  2387.3505859375
               r(r931) =  2380.21630859375
               r(r930) =  2373.086181640625
               r(r929) =  2367.407470703125
               r(r928) =  2358.994140625
               r(r927) =  2354.508056640625
               r(r926) =  2349.89013671875
               r(r925) =  2345.127197265625
               r(r924) =  2340.4765625
               r(r923) =  2333.93603515625
               r(r922) =  2328.04248046875
               r(r921) =  2322.531494140625
               r(r920) =  2316.626953125
               r(r919) =  2310.139892578125
               r(r918) =  2302.5087890625
               r(r917) =  2296.353759765625
               r(r916) =  2292.111572265625
               r(r915) =  2287.626953125
               r(r914) =  2283.822509765625
               r(r913) =  2279.59228515625
               r(r912) =  2271.947509765625
               r(r911) =  2268.146728515625
               r(r910) =  2262.42822265625
               r(r909) =  2258.368408203125
               r(r908) =  2253.54296875
               r(r907) =  2249.46826171875
               r(r906) =  2245.130859375
               r(r905) =  2241.52587890625
               r(r904) =  2234.063720703125
               r(r903) =  2229.434326171875
               r(r902) =  2225.071044921875
               r(r901) =  2221.21826171875
               r(r900) =  2217.73681640625
               r(r899) =  2213.216552734375
               r(r898) =  2208.512939453125
               r(r897) =  2203.038330078125
               r(r896) =  2198.165771484375
               r(r895) =  2193.56201171875
               r(r894) =  2189.415283203125
               r(r893) =  2186.560546875
               r(r892) =  2182.307373046875
               r(r891) =  2178.755615234375
               r(r890) =  2175.8974609375
               r(r889) =  2170.34814453125
               r(r888) =  2166.309326171875
               r(r887) =  2163.10302734375
               r(r886) =  2157.667724609375
               r(r885) =  2154.050048828125
               r(r884) =  2150.00244140625
               r(r883) =  2143.4912109375
               r(r882) =  2138.442626953125
               r(r881) =  2134.957275390625
               r(r880) =  2130.16357421875
               r(r879) =  2125.096923828125
               r(r878) =  2121.81005859375
               r(r877) =  2117.51171875
               r(r876) =  2113.813232421875
               r(r875) =  2111.6025390625
               r(r874) =  2106.134033203125
               r(r873) =  2101.890625
               r(r872) =  2098.66259765625
               r(r871) =  2095.8349609375
               r(r870) =  2092.201171875
               r(r869) =  2090.08154296875
               r(r868) =  2087.758544921875
               r(r867) =  2084.429931640625
               r(r866) =  2080.69091796875
               r(r865) =  2077.4853515625
               r(r864) =  2074.89013671875
               r(r863) =  2071.265380859375
               r(r862) =  2067.565673828125
               r(r861) =  2063.87060546875
               r(r860) =  2060.79345703125
               r(r859) =  2058.545654296875
               r(r858) =  2056.05517578125
               r(r857) =  2053.833984375
               r(r856) =  2050.6025390625
               r(r855) =  2047.930297851563
               r(r854) =  2044.811889648438
               r(r853) =  2042.64697265625
               r(r852) =  2039.103759765625
               r(r851) =  2036.064819335938
               r(r850) =  2032.83349609375
               r(r849) =  2030.946655273438
               r(r848) =  2029.40087890625
               r(r847) =  2027.411254882813
               r(r846) =  2023.948364257813
               r(r845) =  2020.660888671875
               r(r844) =  2016.534545898438
               r(r843) =  2014.859130859375
               r(r842) =  2013.311157226563
               r(r841) =  2011.025390625
               r(r840) =  2008.202026367188
               r(r839) =  2005.3701171875
               r(r838) =  2003.724487304688
               r(r837) =  2002.093994140625
               r(r836) =  1999.717895507813
               r(r835) =  1997.293701171875
               r(r834) =  1994.566162109375
               r(r833) =  1993.0400390625
               r(r832) =  1989.523193359375
               r(r831) =  1987.588500976563
               r(r830) =  1984.30078125
               r(r829) =  1982.552124023438
               r(r828) =  1980.002319335938
               r(r827) =  1978.298706054688
               r(r826) =  1975.105102539063
               r(r825) =  1973.364868164063
               r(r824) =  1971.2890625
               r(r823) =  1967.249633789063
               r(r822) =  1965.347412109375
               r(r821) =  1963.613891601563
               r(r820) =  1960.794921875
               r(r819) =  1957.998779296875
               r(r818) =  1955.646362304688
               r(r817) =  1953.68212890625
               r(r816) =  1951.123657226563
               r(r815) =  1950.36279296875
               r(r814) =  1948.42626953125
               r(r813) =  1945.029296875
               r(r812) =  1942.669799804688
               r(r811) =  1939.641235351563
               r(r810) =  1937.724609375
               r(r809) =  1935.037719726563
               r(r808) =  1932.662841796875
               r(r807) =  1931.031372070313
               r(r806) =  1929.95458984375
               r(r805) =  1927.700439453125
               r(r804) =  1925.608154296875
               r(r803) =  1921.873046875
               r(r802) =  1919.228881835938
               r(r801) =  1916.9970703125
               r(r800) =  1914.7041015625
               r(r799) =  1911.195190429688
               r(r798) =  1908.901733398438
               r(r797) =  1907.146362304688
               r(r796) =  1905.885131835938
               r(r795) =  1902.879150390625
               r(r794) =  1900.735595703125
               r(r793) =  1898.6181640625
               r(r792) =  1896.602661132813
               r(r791) =  1893.982543945313
               r(r790) =  1890.422119140625
               r(r789) =  1888.654663085938
               r(r788) =  1887.155029296875
               r(r787) =  1884.558959960938
               r(r786) =  1882.961669921875
               r(r785) =  1880.98193359375
               r(r784) =  1879.018310546875
               r(r783) =  1876.526611328125
               r(r782) =  1873.984375
               r(r781) =  1871.802856445313
               r(r780) =  1870.130859375
               r(r779) =  1868.646606445313
               r(r778) =  1866.846557617188
               r(r777) =  1864.88427734375
               r(r776) =  1863.108764648438
               r(r775) =  1860.990234375
               r(r774) =  1857.712158203125
               r(r773) =  1856.547607421875
               r(r772) =  1855.336303710938
               r(r771) =  1853.721313476563
               r(r770) =  1851.726684570313
               r(r769) =  1849.278564453125
               r(r768) =  1847.66357421875
               r(r767) =  1845.6435546875
               r(r766) =  1843.45654296875
               r(r765) =  1841.80712890625
               r(r764) =  1839.831787109375
               r(r763) =  1838.531005859375
               r(r762) =  1836.557006835938
               r(r761) =  1834.76416015625
               r(r760) =  1833.528198242188
               r(r759) =  1831.817993164063
               r(r758) =  1830.700561523438
               r(r757) =  1828.883056640625
               r(r756) =  1827.11376953125
               r(r755) =  1825.639770507813
               r(r754) =  1824.036743164063
               r(r753) =  1821.670043945313
               r(r752) =  1819.798095703125
               r(r751) =  1818.262573242188
               r(r750) =  1817.171508789063
               r(r749) =  1815.942016601563
               r(r748) =  1814.92919921875
               r(r747) =  1813.311401367188
               r(r746) =  1811.980224609375
               r(r745) =  1810.812255859375
               r(r744) =  1809.686645507813
               r(r743) =  1807.478759765625
               r(r742) =  1805.297607421875
               r(r741) =  1804.437622070313
               r(r740) =  1802.856201171875
               r(r739) =  1801.3427734375
               r(r738) =  1799.764282226563
               r(r737) =  1798.298828125
               r(r736) =  1797.087768554688
               r(r735) =  1795.45458984375
               r(r734) =  1794.057373046875
               r(r733) =  1792.661499023438
               r(r732) =  1791.093139648438
               r(r731) =  1789.336791992188
               r(r730) =  1788.126831054688
               r(r729) =  1786.064819335938
               r(r728) =  1784.516845703125
               r(r727) =  1783.305297851563
               r(r726) =  1781.423095703125
               r(r725) =  1779.35009765625
               r(r724) =  1778.06591796875
               r(r723) =  1776.785522460938
               r(r722) =  1775.427368164063
               r(r721) =  1774.225219726563
               r(r720) =  1773.390380859375
               r(r719) =  1772.209838867188
               r(r718) =  1770.763549804688
               r(r717) =  1768.856079101563
               r(r716) =  1767.8037109375
               r(r715) =  1766.84521484375
               r(r714) =  1765.678955078125
               r(r713) =  1764.336303710938
               r(r712) =  1762.7509765625
               r(r711) =  1761.58154296875
               r(r710) =  1759.82373046875
               r(r709) =  1758.743530273438
               r(r708) =  1757.644653320313
               r(r707) =  1755.2890625
               r(r706) =  1753.552124023438
               r(r705) =  1751.875366210938
               r(r704) =  1750.35205078125
               r(r703) =  1748.717163085938
               r(r702) =  1746.29345703125
               r(r701) =  1745.063110351563
               r(r700) =  1743.516235351563
               r(r699) =  1742.162231445313
               r(r698) =  1741.50537109375
               r(r697) =  1739.85546875
               r(r696) =  1738.295654296875
               r(r695) =  1736.584350585938
               r(r694) =  1735.213745117188
               r(r693) =  1734.028198242188
               r(r692) =  1732.878784179688
               r(r691) =  1731.755493164063
               r(r690) =  1730.0888671875
               r(r689) =  1728.321655273438
               r(r688) =  1726.706665039063
               r(r687) =  1724.971435546875
               r(r686) =  1723.87353515625
               r(r685) =  1722.493286132813
               r(r684) =  1721.054931640625
               r(r683) =  1720.113891601563
               r(r682) =  1718.62890625
               r(r681) =  1716.501220703125
               r(r680) =  1715.242553710938
               r(r679) =  1714.1875
               r(r678) =  1712.37841796875
               r(r677) =  1711.218383789063
               r(r676) =  1710.012817382813
               r(r675) =  1708.532470703125
               r(r674) =  1707.332641601563
               r(r673) =  1705.99609375
               r(r672) =  1704.696044921875
               r(r671) =  1702.901123046875
               r(r670) =  1701.745727539063
               r(r669) =  1700.565185546875
               r(r668) =  1698.793579101563
               r(r667) =  1697.935668945313
               r(r666) =  1696.2158203125
               r(r665) =  1695.0185546875
               r(r664) =  1693.185791015625
               r(r663) =  1692.346923828125
               r(r662) =  1691.314819335938
               r(r661) =  1690.560668945313
               r(r660) =  1689.556762695313
               r(r659) =  1688.744384765625
               r(r658) =  1687.180786132813
               r(r657) =  1686.591064453125
               r(r656) =  1685.6884765625
               r(r655) =  1684.715942382813
               r(r654) =  1683.952880859375
               r(r653) =  1682.787719726563
               r(r652) =  1681.696533203125
               r(r651) =  1680.29296875
               r(r650) =  1678.800903320313
               r(r649) =  1677.656372070313
               r(r648) =  1676.552368164063
               r(r647) =  1675.41650390625
               r(r646) =  1674.205322265625
               r(r645) =  1673.458862304688
               r(r644) =  1672.314331054688
               r(r643) =  1671.0126953125
               r(r642) =  1669.663330078125
               r(r641) =  1668.460205078125
               r(r640) =  1667.3388671875
               r(r639) =  1666.039306640625
               r(r638) =  1665.042358398438
               r(r637) =  1663.834106445313
               r(r636) =  1662.774658203125
               r(r635) =  1661.900634765625
               r(r634) =  1660.876220703125
               r(r633) =  1660.10302734375
               r(r632) =  1658.776000976563
               r(r631) =  1657.814086914063
               r(r630) =  1656.9951171875
               r(r629) =  1655.686157226563
               r(r628) =  1654.819702148438
               r(r627) =  1653.406005859375
               r(r626) =  1652.39599609375
               r(r625) =  1651.162719726563
               r(r624) =  1649.973388671875
               r(r623) =  1648.5595703125
               r(r622) =  1647.348266601563
               r(r621) =  1646.136962890625
               r(r620) =  1644.880737304688
               r(r619) =  1643.885375976563
               r(r618) =  1642.502319335938
               r(r617) =  1641.023559570313
               r(r616) =  1640.078125
               r(r615) =  1638.734619140625
               r(r614) =  1637.338134765625
               r(r613) =  1636.395263671875
               r(r612) =  1635.022338867188
               r(r611) =  1633.25048828125
               r(r610) =  1632.070068359375
               r(r609) =  1631.149658203125
               r(r608) =  1629.780395507813
               r(r607) =  1629.022094726563
               r(r606) =  1628.0546875
               r(r605) =  1626.882202148438
               r(r604) =  1625.56103515625
               r(r603) =  1624.574340820313
               r(r602) =  1623.279907226563
               r(r601) =  1621.835327148438
               r(r600) =  1620.608276367188
               r(r599) =  1618.96533203125
               r(r598) =  1617.898559570313
               r(r597) =  1617.2724609375
               r(r596) =  1616.643920898438
               r(r595) =  1615.616577148438
               r(r594) =  1613.969116210938
               r(r593) =  1613.356201171875
               r(r592) =  1612.212280273438
               r(r591) =  1610.8134765625
               r(r590) =  1609.874755859375
               r(r589) =  1609.201782226563
               r(r588) =  1608.521728515625
               r(r587) =  1606.962280273438
               r(r586) =  1605.813842773438
               r(r585) =  1604.348876953125
               r(r584) =  1603.627685546875
               r(r583) =  1602.864624023438
               r(r582) =  1601.1474609375
               r(r581) =  1600.194213867188
               r(r580) =  1598.986694335938
               r(r579) =  1597.808837890625
               r(r578) =  1596.917114257813
               r(r577) =  1595.652099609375
               r(r576) =  1594.644409179688
               r(r575) =  1593.242797851563
               r(r574) =  1592.509033203125
               r(r573) =  1591.607666015625
               r(r572) =  1590.081665039063
               r(r571) =  1587.981323242188
               r(r570) =  1587.194213867188
               r(r569) =  1585.700073242188
               r(r568) =  1584.749267578125
               r(r567) =  1583.88623046875
               r(r566) =  1582.864013671875
               r(r565) =  1581.831909179688
               r(r564) =  1580.834838867188
               r(r563) =  1580.105224609375
               r(r562) =  1578.927124023438
               r(r561) =  1578.08642578125
               r(r560) =  1577.085205078125
               r(r559) =  1576.34423828125
               r(r558) =  1575.418090820313
               r(r557) =  1574.483154296875
               r(r556) =  1573.44140625
               r(r555) =  1572.633911132813
               r(r554) =  1571.5078125
               r(r553) =  1570.614990234375
               r(r552) =  1569.607666015625
               r(r551) =  1568.298828125
               r(r550) =  1567.520141601563
               r(r549) =  1566.724609375
               r(r548) =  1566.041625976563
               r(r547) =  1564.813110351563
               r(r546) =  1563.378784179688
               r(r545) =  1562.628662109375
               r(r544) =  1561.52734375
               r(r543) =  1560.622680664063
               r(r542) =  1559.7451171875
               r(r541) =  1558.851196289063
               r(r540) =  1558.064086914063
               r(r539) =  1556.8837890625
               r(r538) =  1555.652099609375
               r(r537) =  1555.268310546875
               r(r536) =  1554.32470703125
               r(r535) =  1553.450805664063
               r(r534) =  1551.962280273438
               r(r533) =  1550.825805664063
               r(r532) =  1549.727905273438
               r(r531) =  1548.813842773438
               r(r530) =  1548.20068359375
               r(r529) =  1547.391845703125
               r(r528) =  1545.46826171875
               r(r527) =  1544.46484375
               r(r526) =  1543.15185546875
               r(r525) =  1542.009643554688
               r(r524) =  1541.4287109375
               r(r523) =  1540.325439453125
               r(r522) =  1538.948364257813
               r(r521) =  1537.803955078125
               r(r520) =  1536.865966796875
               r(r519) =  1535.895385742188
               r(r518) =  1535.0283203125
               r(r517) =  1534.0654296875
               r(r516) =  1533.4814453125
               r(r515) =  1532.461791992188
               r(r514) =  1531.641479492188
               r(r513) =  1530.833984375
               r(r512) =  1529.80712890625
               r(r511) =  1528.645751953125
               r(r510) =  1527.80517578125
               r(r509) =  1526.712280273438
               r(r508) =  1526.190185546875
               r(r507) =  1525.278930664063
               r(r506) =  1524.077514648438
               r(r505) =  1523.19580078125
               r(r504) =  1522.242309570313
               r(r503) =  1521.297241210938
               r(r502) =  1520.5361328125
               r(r501) =  1519.169677734375
               r(r500) =  1518.424865722656
               r(r499) =  1517.579956054688
               r(r498) =  1516.698608398438
               r(r497) =  1516.052856445313
               r(r496) =  1515.108642578125
               r(r495) =  1514.477416992188
               r(r494) =  1513.572509765625
               r(r493) =  1512.458618164063
               r(r492) =  1511.627075195313
               r(r491) =  1510.659790039063
               r(r490) =  1510.035888671875
               r(r489) =  1509.166748046875
               r(r488) =  1508.040283203125
               r(r487) =  1507.086303710938
               r(r486) =  1506.322998046875
               r(r485) =  1505.189575195313
               r(r484) =  1504.414306640625
               r(r483) =  1503.169555664063
               r(r482) =  1502.537719726563
               r(r481) =  1501.379272460938
               r(r480) =  1500.5908203125
               r(r479) =  1499.453735351563
               r(r478) =  1498.323120117188
               r(r477) =  1497.35498046875
               r(r476) =  1496.505615234375
               r(r475) =  1494.890502929688
               r(r474) =  1493.921508789063
               r(r473) =  1493.072998046875
               r(r472) =  1492.127807617188
               r(r471) =  1490.947021484375
               r(r470) =  1490.246704101563
               r(r469) =  1489.236694335938
               r(r468) =  1488.418701171875
               r(r467) =  1487.419067382813
               r(r466) =  1486.861938476563
               r(r465) =  1486.02587890625
               r(r464) =  1485.6015625
               r(r463) =  1484.390258789063
               r(r462) =  1484.126342773438
               r(r461) =  1483.236083984375
               r(r460) =  1482.424072265625
               r(r459) =  1481.158935546875
               r(r458) =  1479.925170898438
               r(r457) =  1479.14013671875
               r(r456) =  1477.562622070313
               r(r455) =  1476.579345703125
               r(r454) =  1475.703002929688
               r(r453) =  1474.807739257813
               r(r452) =  1473.33349609375
               r(r451) =  1472.17041015625
               r(r450) =  1471.264526367188
               r(r449) =  1470.280151367188
               r(r448) =  1469.244995117188
               r(r447) =  1468.545532226563
               r(r446) =  1467.918823242188
               r(r445) =  1465.940551757813
               r(r444) =  1464.803588867188
               r(r443) =  1463.966552734375
               r(r442) =  1463.187255859375
               r(r441) =  1462.249145507813
               r(r440) =  1461.42333984375
               r(r439) =  1460.158447265625
               r(r438) =  1459.408447265625
               r(r437) =  1458.471069335938
               r(r436) =  1457.683959960938
               r(r435) =  1456.770751953125
               r(r434) =  1456.056762695313
               r(r433) =  1455.11083984375
               r(r432) =  1454.235473632813
               r(r431) =  1453.494506835938
               r(r430) =  1452.485595703125
               r(r429) =  1451.755126953125
               r(r428) =  1450.795043945313
               r(r427) =  1449.932373046875
               r(r426) =  1449.219604492188
               r(r425) =  1448.265869140625
               r(r424) =  1447.645385742188
               r(r423) =  1446.825317382813
               r(r422) =  1446.071166992188
               r(r421) =  1445.2685546875
               r(r420) =  1444.102172851563
               r(r419) =  1443.315063476563
               r(r418) =  1442.527954101563
               r(r417) =  1441.740966796875
               r(r416) =  1440.772827148438
               r(r415) =  1439.773071289063
               r(r414) =  1438.818969726563
               r(r413) =  1437.744018554688
               r(r412) =  1436.491821289063
               r(r411) =  1435.625732421875
               r(r410) =  1435.039916992188
               r(r409) =  1434.07373046875
               r(r408) =  1433.438720703125
               r(r407) =  1432.25146484375
               r(r406) =  1431.171630859375
               r(r405) =  1430.062133789063
               r(r404) =  1429.041137695313
               r(r403) =  1428.464721679688
               r(r402) =  1427.647583007813
               r(r401) =  1426.839965820313
               r(r400) =  1425.950439453125
               r(r399) =  1424.853271484375
               r(r398) =  1424.026245117188
               r(r397) =  1423.134399414063
               r(r396) =  1422.694091796875
               r(r395) =  1421.768188476563
               r(r394) =  1420.877807617188
               r(r393) =  1419.695922851563
               r(r392) =  1418.964721679688
               r(r391) =  1418.556762695313
               r(r390) =  1417.827270507813
               r(r389) =  1416.743774414063
               r(r388) =  1415.695068359375
               r(r387) =  1415.12841796875
               r(r386) =  1414.359252929688
               r(r385) =  1413.51220703125
               r(r384) =  1412.606079101563
               r(r383) =  1411.897216796875
               r(r382) =  1411.036499023438
               r(r381) =  1410.079467773438
               r(r380) =  1409.27197265625
               r(r379) =  1408.608154296875
               r(r378) =  1407.454467773438
               r(r377) =  1406.728149414063
               r(r376) =  1405.721801757813
               r(r375) =  1405.233154296875
               r(r374) =  1404.675659179688
               r(r373) =  1403.819458007813
               r(r372) =  1402.966796875
               r(r371) =  1402.049926757813
               r(r370) =  1400.997924804688
               r(r369) =  1400.013427734375
               r(r368) =  1399.378051757813
               r(r367) =  1398.771850585938
               r(r366) =  1397.964233398438
               r(r365) =  1397.407592773438
               r(r364) =  1396.550415039063
               r(r363) =  1396.043823242188
               r(r362) =  1394.935424804688
               r(r361) =  1393.912719726563
               r(r360) =  1392.511596679688
               r(r359) =  1391.911743164063
               r(r358) =  1390.944580078125
               r(r357) =  1390.154418945313
               r(r356) =  1389.482788085938
               r(r355) =  1388.79541015625
               r(r354) =  1388.030151367188
               r(r353) =  1387.023803710938
               r(r352) =  1385.997314453125
               r(r351) =  1385.1015625
               r(r350) =  1384.17529296875
               r(r349) =  1383.021362304688
               r(r348) =  1382.241821289063
               r(r347) =  1381.607543945313
               r(r346) =  1380.724487304688
               r(r345) =  1378.76025390625
               r(r344) =  1378.175048828125
               r(r343) =  1377.576049804688
               r(r342) =  1376.6328125
               r(r341) =  1375.472534179688
               r(r340) =  1374.539794921875
               r(r339) =  1373.731811523438
               r(r338) =  1373.500366210938
               r(r337) =  1372.117309570313
               r(r336) =  1371.080688476563
               r(r335) =  1370.1318359375
               r(r334) =  1369.523559570313
               r(r333) =  1368.52197265625
               r(r332) =  1368.122680664063
               r(r331) =  1366.94775390625
               r(r330) =  1365.453247070313
               r(r329) =  1364.642456054688
               r(r328) =  1363.481079101563
               r(r327) =  1362.617431640625
               r(r326) =  1361.547485351563
               r(r325) =  1360.4013671875
               r(r324) =  1359.395751953125
               r(r323) =  1358.4833984375
               r(r322) =  1357.456787109375
               r(r321) =  1356.35791015625
               r(r320) =  1355.600463867188
               r(r319) =  1354.673461914063
               r(r318) =  1353.877685546875
               r(r317) =  1352.776245117188
               r(r316) =  1351.708618164063
               r(r315) =  1350.970703125
               r(r314) =  1350.413696289063
               r(r313) =  1349.50048828125
               r(r312) =  1348.490478515625
               r(r311) =  1348.00927734375
               r(r310) =  1346.674072265625
               r(r309) =  1345.68994140625
               r(r308) =  1345.057861328125
               r(r307) =  1343.766235351563
               r(r306) =  1342.548095703125
               r(r305) =  1341.359741210938
               r(r304) =  1340.402709960938
               r(r303) =  1339.18701171875
               r(r302) =  1338.177856445313
               r(r301) =  1337.385375976563
               r(r300) =  1336.57763671875
               r(r299) =  1335.887573242188
               r(r298) =  1335.258911132813
               r(r297) =  1334.32080078125
               r(r296) =  1334.077026367188
               r(r295) =  1333.2900390625
               r(r294) =  1332.538818359375
               r(r293) =  1332.21044921875
               r(r292) =  1331.183227539063
               r(r291) =  1330.523071289063
               r(r290) =  1330.023071289063
               r(r289) =  1329.636352539063
               r(r288) =  1329.157104492188
               r(r287) =  1327.976928710938
               r(r286) =  1326.884765625
               r(r285) =  1326.154907226563
               r(r284) =  1325.034790039063
               r(r283) =  1323.83447265625
               r(r282) =  1322.976196289063
               r(r281) =  1321.781982421875
               r(r280) =  1320.73974609375
               r(r279) =  1319.31591796875
               r(r278) =  1318.403442382813
               r(r277) =  1317.233276367188
               r(r276) =  1316.788330078125
               r(r275) =  1315.575805664063
               r(r274) =  1314.574462890625
               r(r273) =  1313.389892578125
               r(r272) =  1312.093872070313
               r(r271) =  1311.335693359375
               r(r270) =  1310.26171875
               r(r269) =  1309.669311523438
               r(r268) =  1308.306884765625
               r(r267) =  1307.703002929688
               r(r266) =  1306.718627929688
               r(r265) =  1305.537841796875
               r(r264) =  1304.845336914063
               r(r263) =  1303.274047851563
               r(r262) =  1302.036376953125
               r(r261) =  1301.037963867188
               r(r260) =  1300.027954101563
               r(r259) =  1299.23974609375
               r(r258) =  1298.65478515625
               r(r257) =  1297.270751953125
               r(r256) =  1296.404174804688
               r(r255) =  1295.494750976563
               r(r254) =  1294.3740234375
               r(r253) =  1293.566528320313
               r(r252) =  1292.35400390625
               r(r251) =  1290.94140625
               r(r250) =  1289.792114257813
               r(r249) =  1288.919799804688
               r(r248) =  1287.824462890625
               r(r247) =  1287.306396484375
               r(r246) =  1285.85546875
               r(r245) =  1285.068359375
               r(r244) =  1284.277587890625
               r(r243) =  1283.296752929688
               r(r242) =  1282.4599609375
               r(r241) =  1280.873657226563
               r(r240) =  1280.147094726563
               r(r239) =  1279.557373046875
               r(r238) =  1278.5390625
               r(r237) =  1277.818603515625
               r(r236) =  1277.223754882813
               r(r235) =  1276.605102539063
               r(r234) =  1275.227172851563
               r(r233) =  1274.242553710938
               r(r232) =  1273.553588867188
               r(r231) =  1272.767211914063
               r(r230) =  1271.644775390625
               r(r229) =  1270.25146484375
               r(r228) =  1269.546142578125
               r(r227) =  1268.449096679688
               r(r226) =  1266.974243164063
               r(r225) =  1265.758911132813
               r(r224) =  1264.203979492188
               r(r223) =  1263.098754882813
               r(r222) =  1262.138671875
               r(r221) =  1261.24267578125
               r(r220) =  1260.247436523438
               r(r219) =  1259.43994140625
               r(r218) =  1257.856567382813
               r(r217) =  1256.135498046875
               r(r216) =  1254.596069335938
               r(r215) =  1252.97802734375
               r(r214) =  1251.88916015625
               r(r213) =  1251.161010742188
               r(r212) =  1250.341064453125
               r(r211) =  1249.24658203125
               r(r210) =  1247.868530273438
               r(r209) =  1246.314575195313
               r(r208) =  1244.539184570313
               r(r207) =  1243.379028320313
               r(r206) =  1241.8720703125
               r(r205) =  1240.862670898438
               r(r204) =  1238.179321289063
               r(r203) =  1236.255737304688
               r(r202) =  1235.312133789063
               r(r201) =  1233.904663085938
               r(r200) =  1232.914306640625
               r(r199) =  1231.96826171875
               r(r198) =  1230.349609375
               r(r197) =  1229.28955078125
               r(r196) =  1228.103515625
               r(r195) =  1227.342529296875
               r(r194) =  1225.858520507813
               r(r193) =  1225.23388671875
               r(r192) =  1224.643432617188
               r(r191) =  1223.462280273438
               r(r190) =  1221.717529296875
               r(r189) =  1220.265258789063
               r(r188) =  1219.255859375
               r(r187) =  1218.3447265625
               r(r186) =  1216.918579101563
               r(r185) =  1215.8232421875
               r(r184) =  1214.739013671875
               r(r183) =  1212.628051757813
               r(r182) =  1211.582397460938
               r(r181) =  1210.471923828125
               r(r180) =  1209.016723632813
               r(r179) =  1208.111328125
               r(r178) =  1206.929443359375
               r(r177) =  1205.74755859375
               r(r176) =  1204.142456054688
               r(r175) =  1203.77978515625
               r(r174) =  1202.20556640625
               r(r173) =  1200.630737304688
               r(r172) =  1198.81298828125
               r(r171) =  1198.072021484375
               r(r170) =  1197.131713867188
               r(r169) =  1195.83203125
               r(r168) =  1194.332153320313
               r(r167) =  1193.483642578125
               r(r166) =  1192.514526367188
               r(r165) =  1190.78369140625
               r(r164) =  1189.171630859375
               r(r163) =  1187.7548828125
               r(r162) =  1186.744995117188
               r(r161) =  1185.35009765625
               r(r160) =  1184.12890625
               r(r159) =  1182.926147460938
               r(r158) =  1181.104248046875
               r(r157) =  1179.67724609375
               r(r156) =  1177.011474609375
               r(r155) =  1174.846435546875
               r(r154) =  1173.417724609375
               r(r153) =  1172.010986328125
               r(r152) =  1169.984497070313
               r(r151) =  1168.411376953125
               r(r150) =  1167.28076171875
               r(r149) =  1165.744384765625
               r(r148) =  1164.414794921875
               r(r147) =  1163.523071289063
               r(r146) =  1161.503662109375
               r(r145) =  1159.990478515625
               r(r144) =  1158.180541992188
               r(r143) =  1156.933227539063
               r(r142) =  1155.849731445313
               r(r141) =  1154.307006835938
               r(r140) =  1153.162475585938
               r(r139) =  1152.415283203125
               r(r138) =  1151.382568359375
               r(r137) =  1149.185913085938
               r(r136) =  1148.016357421875
               r(r135) =  1147.258911132813
               r(r134) =  1145.518188476563
               r(r133) =  1144.195068359375
               r(r132) =  1142.92626953125
               r(r131) =  1141.714599609375
               r(r130) =  1140.351928710938
               r(r129) =  1137.9169921875
               r(r128) =  1137.843383789063
               r(r127) =  1135.074096679688
               r(r126) =  1133.720703125
               r(r125) =  1132.67236328125
               r(r124) =  1130.560791015625
               r(r123) =  1129.121826171875
               r(r122) =  1127.258056640625
               r(r121) =  1125.892578125
               r(r120) =  1123.970458984375
               r(r119) =  1121.925048828125
               r(r118) =  1119.989013671875
               r(r117) =  1119.300048828125
               r(r116) =  1116.47314453125
               r(r115) =  1115.208740234375
               r(r114) =  1112.615478515625
               r(r113) =  1110.2060546875
               r(r112) =  1108.597900390625
               r(r111) =  1106.417602539063
               r(r110) =  1105.081787109375
               r(r109) =  1102.792236328125
               r(r108) =  1100.484375
               r(r107) =  1098.613525390625
               r(r106) =  1096.007080078125
               r(r105) =  1092.967529296875
               r(r104) =  1091.195922851563
               r(r103) =  1087.965209960938
               r(r102) =  1086.385375976563
               r(r101) =  1082.2236328125
               r(r100) =  1079.977172851563
                r(r99) =  1077.655517578125
                r(r98) =  1075.683227539063
                r(r97) =  1072.24560546875
                r(r96) =  1069.151611328125
                r(r95) =  1066.4462890625
                r(r94) =  1062.759765625
                r(r93) =  1060.81787109375
                r(r92) =  1058.680786132813
                r(r91) =  1055.89404296875
                r(r90) =  1053.265747070313
                r(r89) =  1050.1708984375
                r(r88) =  1047.698120117188
                r(r87) =  1044.409423828125
                r(r86) =  1040.1435546875
                r(r85) =  1036.912841796875
                r(r84) =  1033.329711914063
                r(r83) =  1029.58984375
                r(r82) =  1026.244018554688
                r(r81) =  1023.786926269531
                r(r80) =  1019.748596191406
                r(r79) =  1016.2060546875
                r(r78) =  1012.882873535156
                r(r77) =  1009.652221679688
                r(r76) =  1006.547546386719
                r(r75) =  1001.433837890625
                r(r74) =  997.812744140625
                r(r73) =  994.5073852539063
                r(r72) =  990.0328369140625
                r(r71) =  986.2887573242188
                r(r70) =  982.095947265625
                r(r69) =  977.1691284179688
                r(r68) =  970.6792602539063
                r(r67) =  965.8186645507813
                r(r66) =  963.0315551757813
                r(r65) =  960.2686767578125
                r(r64) =  956.2111206054688
                r(r63) =  953.418701171875
                r(r62) =  947.6682739257813
                r(r61) =  942.5250244140625
                r(r60) =  937.4175415039063
                r(r59) =  932.653076171875
                r(r58) =  926.6889038085938
                r(r57) =  921.7799072265625
                r(r56) =  917.515380859375
                r(r55) =  913.2666625976563
                r(r54) =  908.485107421875
                r(r53) =  900.0790405273438
                r(r52) =  893.584228515625
                r(r51) =  888.1004028320313
                r(r50) =  884.0387573242188
                r(r49) =  877.361083984375
                r(r48) =  872.3887939453125
                r(r47) =  867.0890502929688
                r(r46) =  862.5819091796875
                r(r45) =  855.1629638671875
                r(r44) =  849.6094360351563
                r(r43) =  842.5702514648438
                r(r42) =  836.1110229492188
                r(r41) =  829.7318115234375
                r(r40) =  825.244873046875
                r(r39) =  817.7451171875
                r(r38) =  810.1305541992188
                r(r37) =  804.7440185546875
                r(r36) =  799.0385131835938
                r(r35) =  791.5404663085938
                r(r34) =  787.2984619140625
                r(r33) =  779.1863403320313
                r(r32) =  775.2401733398438
                r(r31) =  768.7972412109375
                r(r30) =  759.536376953125
                r(r29) =  755.8255004882813
                r(r28) =  747.5028076171875
                r(r27) =  735.927734375
                r(r26) =  726.6768188476563
                r(r25) =  721.0702514648438
                r(r24) =  714.429443359375
                r(r23) =  709.1595458984375
                r(r22) =  703.2081909179688
                r(r21) =  695.3815307617188
r; t=0.11 15:33:23
. 
.         *full sample - normal distribution fit
.         dpplot wnc_offer_beg_monthly if wnc_offer_beg_monthly<=$incmax, caption("") mlw(medthin) ms(oh) scheme(economist)
r; t=5.40 15:33:28

.         graph export results/${samplefolder}/5_sample${sample}_wnc_offer_dpplot.pdf, replace
file results/two/5_sample2_wnc_offer_dpplot.pdf saved as PDF format
r; t=0.48 15:33:29

.         kdensity wnc_offer_beg_monthly, normal
r; t=3.49 15:33:32

.         graph export results/${samplefolder}/5_sample${sample}_wnc_offer_kdensity.pdf, replace
file results/two/5_sample2_wnc_offer_kdensity.pdf saved as PDF format
r; t=0.13 15:33:32

.         qnorm wnc_offer_beg_monthly 
r; t=3.90 15:33:36

.         graph export results/${samplefolder}/5_sample${sample}_wnc_offer_qnorm.pdf, replace
file results/two/5_sample2_wnc_offer_qnorm.pdf saved as PDF format
r; t=0.77 15:33:37

.         sktest wnc_offer_beg_monthly

Skewness and kurtosis tests for normality
                                                                  ----- Joint test -----
             Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
----------------------+-----------------------------------------------------------------
wnc_offer_beg_monthly |    15,390         0.0000         0.0000             .          .
r; t=0.35 15:33:37

. 
.         *full sample - lognormal distribution parameters
.         lognfit wnc_offer_beg_monthly, stats

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -1553536.3
rescale:       log likelihood = -153591.03
rescale eq:    log likelihood = -131197.39
Iteration 0:   log likelihood = -131197.39  (not concave)
Iteration 1:   log likelihood = -118038.19  
Iteration 2:   log likelihood = -117346.46  
Iteration 3:   log likelihood = -117289.38  
Iteration 4:   log likelihood = -117289.35  
Iteration 5:   log likelihood = -117289.35  

ML fit of lognormal distribution                  Number of obs   =      15390
                                                  Wald chi2(0)    =          .
Log likelihood = -117289.35                       Prob > chi2     =          .

------------------------------------------------------------------------------
wnc_offer_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.325745   .0026208  2795.24   0.000     7.320608    7.330881
-------------+----------------------------------------------------------------
v            |
       _cons |   .3251259   .0018532   175.44   0.000     .3214937    .3287581
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wnc_offer_beg_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  712.93393       0.00401
 5%  889.76749       0.02442
10%   1.00e+03       0.05406
20%   1.16e+03       0.12166
25%   1.22e+03       0.15875
30%   1.28e+03       0.19779
40%   1.40e+03       0.28147 Mode         1.37e+03
50%   1.52e+03       0.37254 Mean         1.60e+03
60%   1.65e+03       0.47139 Std. Dev.   534.70481
70%   1.80e+03       0.57898
75%   1.89e+03       0.63659 Variance     2.86e+05
80%   2.00e+03       0.69725 Half CV^2     0.05575
90%   2.30e+03       0.83057 Gini coeff.   0.18183
95%   2.59e+03       0.90654 p90/p10       2.30097
99%   3.24e+03       0.97732 p75/p25       1.55052
------------------------------------------------------------
r; t=29.31 15:34:07

.         preserve
r; t=0.69 15:34:07

.         keep if e(sample)==1
(1,012,146 observations deleted)
r; t=0.25 15:34:08

.         if ${sample}<7{
.                 save ${data}\wage_sample_wnc_estimation.dta, replace
file \\iab.baintern.de\DFS\017\Ablagen\D01700-Projekte\D01700-COAL\data\wage_sample_wnc_estimation.dta saved
r; t=0.43 15:34:08
.         }
r; t=0.44 15:34:08

.         if ${sample}==7{
.                 save ${data}\wage_sample7_wnc_estimation.dta, replace
r; t=0.00 15:34:08
.         }
r; t=0.00 15:34:08

.         if ${sample}==8{
.                 save ${data}\wage_sample8_wnc_estimation.dta, replace
r; t=0.00 15:34:08
.         }
r; t=0.01 15:34:08

.         if ${sample}==9{
.                 save ${data}\wage_sample9_wnc_estimation.dta, replace
r; t=0.00 15:34:08
.         }
r; t=0.00 15:34:08

. 
.         tab posttrans

                              posttrans |      Freq.     Percent        Cum.
----------------------------------------+-----------------------------------
        Not post trans'n out of lignite |     15,390      100.00      100.00
----------------------------------------+-----------------------------------
                                  Total |     15,390      100.00
r; t=0.00 15:34:08

.         restore
r; t=0.07 15:34:08

.         
.         putexcel set results/${samplefolder}/5_sample${sample}_wages_distribution, sheet("wnc_offer_lognormal") modify
r; t=0.01 15:34:08

.         putexcel A2=("wnc_offer_beg_monthly_all") B1=("mu") B2=(e(bm)) C1=("sigma") C2=(e(bv)) 
file results/two/5_sample2_wages_distribution.xlsx saved
r; t=0.13 15:34:08

.         `putexcelclose'
r; t=0.00 15:34:08

.         *full sample - lognormal distribution fit
.         dpplot wnc_offer_beg_monthly if wnc_offer_beg_monthly<=$incmax, dist(lognormal) param(`e(bm)' `e(bv)') caption("") mlw(medthin) ms(oh) scheme(economist)
r; t=5.55 15:34:14

.         graph export results/${samplefolder}/5_sample${sample}_wnc_offer_dpplot_logn.pdf, replace
file results/two/5_sample2_wnc_offer_dpplot_logn.pdf saved as PDF format
r; t=0.56 15:34:14

.         
.         * by cells
.         forvalues i = 1/$cellnumber {
  2.         di "Distribution of wage offer after unemployment for cell `i'" 
  3.                         local j = `i'+2
  4.                         *by cells - normal distribution paramaters
.                         capture noisily estpost sum wnc_offer_beg_monthly if cell == `i' , d 
  5.                         if _rc!=2000{
  6.                                 putexcel set results/${samplefolder}/5_sample${sample}_wages_distribution, sheet("wnc_offer_normal") modify
  7.                                 #delimit ; 
delimiter now ;
.                                 putexcel A`j'=("wnc_offer_beg_monthly_`i'") B`j'=matrix(e(mean)) C`j'=matrix(e(sd)) D`j'=matrix(e(sum_w)) E`j'=matrix(e(skewness)) 
>                                 F`j'=matrix(e(kurtosis)) G`j'=matrix(e(sum)) H`j'=matrix(e(min)) I`j'=matrix(e(max)) J`j'=matrix(e(p1)) K`j'=matrix(e(p5)) 
>                                 L`j'=matrix(e(p10)) M`j'=matrix(e(p25)) N`j'=matrix(e(p50)) O`j'=matrix(e(p75)) P`j'=matrix(e(p90)) Q`j'=matrix(e(p95)) R`j'=matrix(e(p99)) ;
  8.                                 #delimit cr
delimiter now cr
.                                 `putexcelclose'
  9.                         }
 10.                         *by cells - normal distribution fit
.                         capture noisily dpplot wnc_offer_beg_monthly if cell == `i' & wnc_offer_beg_monthly<=$incmax, caption("")
 11.                         if _rc!=2000 & _rc!=2001 {
 12.                                 graph export results/${samplefolder}/5_sample${sample}_wnc_offer_dpplot_`i'.pdf, replace
 13.                         }
 14.                         capture noisily kdensity wnc_offer_beg_monthly if cell == `i', normal
 15.                         if _rc!=2000 & _rc!=2001 & _rc!=198{
 16.                                 graph export results/${samplefolder}/5_sample${sample}_wnc_offer_kdensity_`i'.pdf, replace
 17.                         }
 18.                         capture noisily qnorm wnc_offer_beg_monthly if cell == `i'
 19.                         if _rc!=2000 & _rc!=2001 {
 20.                                 graph export results/${samplefolder}/5_sample${sample}_wnc_offer_qnorm_`i'.pdf, replace
 21.                         }
 22.                         capture noisily sktest wnc_offer_beg_monthly if cell == `i'
 23.                         *by cells - lognormal distribution parameters
.                         capture noisily lognfit wnc_offer_beg_monthly if cell == `i', stats
 24.                         if _rc!=2000 & _rc!=2001 {
 25.                                 putexcel set results/${samplefolder}/5_sample${sample}_wages_distribution, sheet("wnc_offer_lognormal") modify
 26.                                 putexcel A`j'=("wnc_offer_beg_monthly_`i'") B`j'=(e(bm)) C`j'=(e(bv))  
 27.                                 `putexcelclose'
 28.                         }
 29.                         *by cells - lognormal distribution fit
.                         capture noisily dpplot wnc_offer_beg_monthly if cell == `i' & wnc_offer_beg_monthly<=$incmax, dist(lognormal) param(`e(bm)' `e(bv)') caption("") scheme(economist)
 30.                         if _rc!=2000 & _rc!=2001 {
 31.                         graph export results/${samplefolder}/5_sample${sample}_wnc_offer_dpplot_logn_`i'.pdf, replace
 32.                         }
 33.         }
Distribution of wage offer after unemployment for cell 1

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wnc_offer_~y |       132        132   1593.294   223058.6   472.2908   1.077144    5.21861   210314.8    675.071   3631.415   711.8544   961.9428   1076.667   1316.696   1502.664   1807.193 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wnc_offer_~y |  2242.235   2554.513   2904.696 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_1.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_kdensity_1.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_qnorm_1.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                                  ----- Joint test -----
             Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
----------------------+-----------------------------------------------------------------
wnc_offer_beg_monthly |       132         0.0000         0.0011         23.79     0.0000

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -13342.785
rescale:       log likelihood = -1318.2847
rescale eq:    log likelihood = -1124.0599
Iteration 0:   log likelihood = -1124.0599  (not concave)
Iteration 1:   log likelihood = -1003.9803  
Iteration 2:   log likelihood = -1000.7208  
Iteration 3:   log likelihood =  -991.0732  
Iteration 4:   log likelihood = -990.86633  
Iteration 5:   log likelihood = -990.86621  
Iteration 6:   log likelihood = -990.86621  

ML fit of lognormal distribution                  Number of obs   =        132
                                                  Wald chi2(0)    =          .
Log likelihood = -990.86621                       Prob > chi2     =          .

------------------------------------------------------------------------------
wnc_offer_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.332194   .0250728   292.44   0.000     7.283052    7.381336
-------------+----------------------------------------------------------------
v            |
       _cons |   .2880647   .0177292    16.25   0.000     .2533162    .3228132
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wnc_offer_beg_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  782.15701       0.00447
 5%  951.81427       0.02662
10%   1.06e+03       0.05825
20%   1.20e+03       0.12930
25%   1.26e+03       0.16789
30%   1.31e+03       0.20826
40%   1.42e+03       0.29411 Mode         1.41e+03
50%   1.53e+03       0.38665 Mean         1.59e+03
60%   1.64e+03       0.48615 Std. Dev.   468.71892
70%   1.78e+03       0.59341
75%   1.86e+03       0.65041 Variance     2.20e+05
80%   1.95e+03       0.71006 Half CV^2     0.04326
90%   2.21e+03       0.83976 Gini coeff.   0.16141
95%   2.46e+03       0.91258 p90/p10       2.09246
99%   2.99e+03       0.97924 p75/p25       1.47490
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_logn_1.pdf saved as PDF format
Distribution of wage offer after unemployment for cell 2

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   1951.792   482976.7   694.9653   .4301782   2.838752   62457.35   733.1725   3586.738   733.1725   898.5768   981.5135   1486.741    1986.24   2317.582 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wnc_offer_~y |  2833.225   3256.681   3586.738 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_2.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_kdensity_2.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_qnorm_2.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                                  ----- Joint test -----
             Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
----------------------+-----------------------------------------------------------------
wnc_offer_beg_monthly |        NA         0.2595         0.7922          1.44     0.4878

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood =  -3402.124
rescale:       log likelihood = -326.57506
rescale eq:    log likelihood = -271.84571
Iteration 0:   log likelihood = -271.84571  (not concave)
Iteration 1:   log likelihood =    -256.33  
Iteration 2:   log likelihood = -254.32396  
Iteration 3:   log likelihood = -254.28323  
Iteration 4:   log likelihood = -254.28308  
Iteration 5:   log likelihood = -254.28308  

ML fit of lognormal distribution                  Number of obs   =         32
                                                  Wald chi2(0)    =          .
Log likelihood = -254.28308                       Prob > chi2     =          .

------------------------------------------------------------------------------
wnc_offer_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.510707   .0661278   113.58   0.000     7.381099    7.640315
-------------+----------------------------------------------------------------
v            |
       _cons |   .3740751   .0467594     8.00   0.000     .2824284    .4657218
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wnc_offer_beg_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  765.46064       0.00346
 5%  987.72845       0.02175
10%   1.13e+03       0.04890
20%   1.33e+03       0.11205
25%   1.42e+03       0.14719
30%   1.50e+03       0.18447
40%   1.66e+03       0.26519 Mode         1.59e+03
50%   1.83e+03       0.35417 Mean         1.96e+03
60%   2.01e+03       0.45195 Std. Dev.   759.57936
70%   2.22e+03       0.55975
75%   2.35e+03       0.61807 Variance     5.77e+05
80%   2.50e+03       0.67995 Half CV^2     0.07510
90%   2.95e+03       0.81792 Gini coeff.   0.20861
95%   3.38e+03       0.89810 p90/p10       2.60855
99%   4.36e+03       0.97455 p75/p25       1.65636
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_logn_2.pdf saved as PDF format
Distribution of wage offer after unemployment for cell 3

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wnc_offer_~y |       174        174   1583.845   225977.7   475.3711   1.109019   4.986031   275588.9   541.8564   3372.253   622.7535   980.4932   1087.799   1330.929   1485.377   1766.501 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wnc_offer_~y |  2274.506   2431.373   3215.742 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_3.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_kdensity_3.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_qnorm_3.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                                  ----- Joint test -----
             Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
----------------------+-----------------------------------------------------------------
wnc_offer_beg_monthly |       174         0.0000         0.0007         29.46     0.0000

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -17555.061
rescale:       log likelihood =  -1736.306
rescale eq:    log likelihood = -1481.4925
Iteration 0:   log likelihood = -1481.4925  (not concave)
Iteration 1:   log likelihood = -1322.7286  
Iteration 2:   log likelihood = -1320.0677  
Iteration 3:   log likelihood = -1307.2483  
Iteration 4:   log likelihood = -1306.9713  
Iteration 5:   log likelihood = -1306.9711  
Iteration 6:   log likelihood = -1306.9711  

ML fit of lognormal distribution                  Number of obs   =        174
                                                  Wald chi2(0)    =          .
Log likelihood = -1306.9711                       Prob > chi2     =          .

------------------------------------------------------------------------------
wnc_offer_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.325327   .0220937   331.56   0.000     7.282024    7.368629
-------------+----------------------------------------------------------------
v            |
       _cons |   .2914355   .0156226    18.65   0.000     .2608158    .3220552
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wnc_offer_beg_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  770.73625       0.00443
 5%  940.07330       0.02642
10%   1.05e+03       0.05786
20%   1.19e+03       0.12860
25%   1.25e+03       0.16704
30%   1.30e+03       0.20730
40%   1.41e+03       0.29295 Mode         1.39e+03
50%   1.52e+03       0.38536 Mean         1.58e+03
60%   1.63e+03       0.48481 Std. Dev.   471.65196
70%   1.77e+03       0.59211
75%   1.85e+03       0.64916 Variance     2.22e+05
80%   1.94e+03       0.70890 Half CV^2     0.04432
90%   2.21e+03       0.83894 Gini coeff.   0.16327
95%   2.45e+03       0.91204 p90/p10       2.11061
99%   2.99e+03       0.97907 p75/p25       1.48163
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_logn_3.pdf saved as PDF format
Distribution of wage offer after unemployment for cell 4

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   2063.963   287787.6   536.4584    .504206   4.281491   173372.9   623.8964   3901.747   623.8964   1270.262   1413.512    1777.87   2018.509   2298.203 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wnc_offer_~y |  2692.151   3048.028   3901.747 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_4.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_kdensity_4.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_qnorm_4.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                                  ----- Joint test -----
             Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
----------------------+-----------------------------------------------------------------
wnc_offer_beg_monthly |        NA         0.0526         0.0345          7.43     0.0244

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -9131.4764
rescale:       log likelihood =  -865.9458
rescale eq:    log likelihood =  -697.1361
Iteration 0:   log likelihood =  -697.1361  (not concave)
Iteration 1:   log likelihood = -651.12086  
Iteration 2:   log likelihood = -650.28506  
Iteration 3:   log likelihood = -648.99333  
Iteration 4:   log likelihood = -648.99151  
Iteration 5:   log likelihood = -648.99151  

ML fit of lognormal distribution                  Number of obs   =         NA
                                                  Wald chi2(0)    =          .
Log likelihood = -648.99151                       Prob > chi2     =          .

------------------------------------------------------------------------------
wnc_offer_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |    7.59696   .0300402   252.89   0.000     7.538083    7.655838
-------------+----------------------------------------------------------------
v            |
       _cons |   .2753232   .0212416    12.96   0.000     .2336904    .3169561
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wnc_offer_beg_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%   1.05e+03       0.00464
 5%   1.27e+03       0.02742
10%   1.40e+03       0.05975
20%   1.58e+03       0.13201
25%   1.65e+03       0.17110
30%   1.72e+03       0.21194
40%   1.86e+03       0.29852 Mode         1.85e+03
50%   1.99e+03       0.39153 Mean         2.07e+03
60%   2.14e+03       0.49123 Std. Dev.   580.63529
70%   2.30e+03       0.59835
75%   2.40e+03       0.65511 Variance     3.37e+05
80%   2.51e+03       0.71440 Half CV^2     0.03937
90%   2.84e+03       0.84285 Gini coeff.   0.15436
95%   3.13e+03       0.91458 p90/p10       2.02523
99%   3.78e+03       0.97987 p75/p25       1.44977
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_logn_4.pdf saved as PDF format
Distribution of wage offer after unemployment for cell 5

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   1486.905     184410   429.4298   .5431663   5.031753    95161.9   324.3506   2851.424   324.3506   1062.202   1135.051   1242.956   1415.603   1608.917 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wnc_offer_~y |  2187.501   2332.219   2851.424 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_5.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_kdensity_5.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_qnorm_5.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                                  ----- Joint test -----
             Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
----------------------+-----------------------------------------------------------------
wnc_offer_beg_monthly |        NA         0.0644         0.0108          8.61     0.0135

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -6335.9734
rescale:       log likelihood = -633.35432
rescale eq:    log likelihood = -544.58641
Iteration 0:   log likelihood = -544.58641  (not concave)
Iteration 1:   log likelihood = -490.05706  
Iteration 2:   log likelihood = -486.05313  
Iteration 3:   log likelihood = -485.95876  
Iteration 4:   log likelihood =  -485.9587  
Iteration 5:   log likelihood =  -485.9587  

ML fit of lognormal distribution                  Number of obs   =         NA
                                                  Wald chi2(0)    =          .
Log likelihood =  -485.9587                       Prob > chi2     =          .

------------------------------------------------------------------------------
wnc_offer_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.256033   .0423703   171.25   0.000     7.172989    7.339077
-------------+----------------------------------------------------------------
v            |
       _cons |   .3389621   .0299603    11.31   0.000      .280241    .3976833
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wnc_offer_beg_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  643.86516       0.00385
 5%  811.18008       0.02364
10%  917.48454       0.05256
20%   1.07e+03       0.11888
25%   1.13e+03       0.15542
30%   1.19e+03       0.19397
40%   1.30e+03       0.27682 Mode         1.26e+03
50%   1.42e+03       0.36732 Mean         1.50e+03
60%   1.54e+03       0.46589 Std. Dev.   523.53968
70%   1.69e+03       0.57356
75%   1.78e+03       0.63139 Variance     2.74e+05
80%   1.88e+03       0.69240 Half CV^2     0.06088
90%   2.19e+03       0.82705 Gini coeff.   0.18942
95%   2.47e+03       0.90421 p90/p10       2.38404
99%   3.12e+03       0.97656 p75/p25       1.57973
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_logn_5.pdf saved as PDF format
Distribution of wage offer after unemployment for cell 6

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   2013.005   586851.7   766.0625   2.759956   15.46265   100650.2   551.9684   5853.678   551.9684   722.3021   1670.795   1735.532   1956.199   2137.498 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wnc_offer_~y |  2364.731   2918.279   5853.678 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_6.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_kdensity_6.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_qnorm_6.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                                  ----- Joint test -----
             Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
----------------------+-----------------------------------------------------------------
wnc_offer_beg_monthly |        NA         0.0000         0.0000         38.54     0.0000

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -5366.1025
rescale:       log likelihood = -512.40334
rescale eq:    log likelihood = -422.37462
Iteration 0:   log likelihood = -422.37462  (not concave)
Iteration 1:   log likelihood = -399.24789  
Iteration 2:   log likelihood = -398.97194  
Iteration 3:   log likelihood = -397.55221  
Iteration 4:   log likelihood = -397.54964  
Iteration 5:   log likelihood = -397.54964  

ML fit of lognormal distribution                  Number of obs   =         NA
                                                  Wald chi2(0)    =          .
Log likelihood = -397.54964                       Prob > chi2     =          .

------------------------------------------------------------------------------
wnc_offer_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.545819   .0513148   147.05   0.000     7.445243    7.646394
-------------+----------------------------------------------------------------
v            |
       _cons |   .3628505    .036285    10.00   0.000     .2917331    .4339679
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wnc_offer_beg_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  813.78982       0.00358
 5%   1.04e+03       0.02234
10%   1.19e+03       0.05005
20%   1.39e+03       0.11420
25%   1.48e+03       0.14979
30%   1.56e+03       0.18747
40%   1.73e+03       0.26888 Mode         1.66e+03
50%   1.89e+03       0.35836 Mean         2.02e+03
60%   2.08e+03       0.45640 Std. Dev.   758.36189
70%   2.29e+03       0.56417
75%   2.42e+03       0.62234 Variance     5.75e+05
80%   2.57e+03       0.68395 Half CV^2     0.07036
90%   3.01e+03       0.82087 Gini coeff.   0.20249
95%   3.44e+03       0.90008 p90/p10       2.53457
99%   4.40e+03       0.97521 p75/p25       1.63146
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_logn_6.pdf saved as PDF format
Distribution of wage offer after unemployment for cell 7

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wnc_offer_~y |      2019       2019   1606.344   178628.7   422.6449   1.261676   7.662022    3243208   271.7984   4463.392   690.0703   1055.825   1174.225   1356.713   1550.778   1795.735 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wnc_offer_~y |  2096.641   2366.826   3008.049 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_7.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_kdensity_7.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_qnorm_7.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                                  ----- Joint test -----
             Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
----------------------+-----------------------------------------------------------------
wnc_offer_beg_monthly |     2,019         0.0000         0.0000        433.73     0.0000

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -204944.67
rescale:       log likelihood = -20201.918
rescale eq:    log likelihood = -17190.372
Iteration 0:   log likelihood = -17190.372  (not concave)
Iteration 1:   log likelihood = -15332.154  
Iteration 2:   log likelihood = -15050.268  
Iteration 3:   log likelihood = -15009.037  
Iteration 4:   log likelihood = -15008.985  
Iteration 5:   log likelihood = -15008.985  

ML fit of lognormal distribution                  Number of obs   =       2019
                                                  Wald chi2(0)    =          .
Log likelihood = -15008.985                       Prob > chi2     =          .

------------------------------------------------------------------------------
wnc_offer_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.348177   .0058669  1252.47   0.000     7.336678    7.359676
-------------+----------------------------------------------------------------
v            |
       _cons |   .2636206   .0041485    63.55   0.000     .2554896    .2717516
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wnc_offer_beg_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  841.26225       0.00480
 5%   1.01e+03       0.02816
10%   1.11e+03       0.06115
20%   1.24e+03       0.13453
25%   1.30e+03       0.17409
30%   1.35e+03       0.21534
40%   1.45e+03       0.30259 Mode         1.45e+03
50%   1.55e+03       0.39604 Mean         1.61e+03
60%   1.66e+03       0.49590 Std. Dev.   431.45140
70%   1.78e+03       0.60287
75%   1.86e+03       0.65942 Variance     1.86e+05
80%   1.94e+03       0.71837 Half CV^2     0.03598
90%   2.18e+03       0.84564 Gini coeff.   0.14788
95%   2.40e+03       0.91640 p90/p10       1.96538
99%   2.87e+03       0.98043 p75/p25       1.42706
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_logn_7.pdf saved as PDF format
Distribution of wage offer after unemployment for cell 8

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wnc_offer_~y |       353        353    1997.54   331218.1   575.5155   .8227281   6.062638   705131.5   338.9832   5091.848   557.8038    1189.93   1396.554    1664.31   1950.979    2294.74 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wnc_offer_~y |  2682.565   3011.622   3710.702 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_8.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_kdensity_8.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_qnorm_8.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                                  ----- Joint test -----
             Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
----------------------+-----------------------------------------------------------------
wnc_offer_beg_monthly |       353         0.0000         0.0000         48.86     0.0000

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -37956.554
rescale:       log likelihood = -3621.1591
rescale eq:    log likelihood = -2957.1065
Iteration 0:   log likelihood = -2957.1065  (not concave)
Iteration 1:   log likelihood = -2778.9307  
Iteration 2:   log likelihood = -2761.5928  
Iteration 3:   log likelihood = -2761.5482  
Iteration 4:   log likelihood = -2761.5482  

ML fit of lognormal distribution                  Number of obs   =        353
                                                  Wald chi2(0)    =          .
Log likelihood = -2761.5482                       Prob > chi2     =          .

------------------------------------------------------------------------------
wnc_offer_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.554953   .0168393   448.65   0.000     7.521948    7.587957
-------------+----------------------------------------------------------------
v            |
       _cons |    .316381   .0119072    26.57   0.000     .2930434    .3397186
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wnc_offer_beg_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  915.01490       0.00411
 5%   1.14e+03       0.02493
10%   1.27e+03       0.05503
20%   1.46e+03       0.12343
25%   1.54e+03       0.16087
30%   1.62e+03       0.20024
40%   1.76e+03       0.28443 Mode         1.73e+03
50%   1.91e+03       0.37586 Mean         2.01e+03
60%   2.07e+03       0.47487 Std. Dev.   651.59667
70%   2.25e+03       0.58239
75%   2.36e+03       0.63987 Variance     4.25e+05
80%   2.49e+03       0.70029 Half CV^2     0.05264
90%   2.87e+03       0.83277 Gini coeff.   0.17702
95%   3.21e+03       0.90799 p90/p10       2.24997
99%   3.99e+03       0.97778 p75/p25       1.53233
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_logn_8.pdf saved as PDF format
Distribution of wage offer after unemployment for cell 9

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wnc_offer_~y |      4811       4811     1667.6   196131.5   442.8673   1.868701   13.84501    8022822   291.3856   6663.412   792.9808   1137.843    1229.48   1398.368   1603.819   1849.076 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wnc_offer_~y |  2178.492   2450.627   3049.279 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_9.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_kdensity_9.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_qnorm_9.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                                  ----- Joint test -----
             Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
----------------------+-----------------------------------------------------------------
wnc_offer_beg_monthly |     4,811         0.0000         0.0000             .          .

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -493622.94
rescale:       log likelihood = -48363.618
rescale eq:    log likelihood = -40863.437
Iteration 0:   log likelihood = -40863.437  (not concave)
Iteration 1:   log likelihood =  -36567.82  
Iteration 2:   log likelihood = -35834.902  
Iteration 3:   log likelihood = -35778.982  
Iteration 4:   log likelihood = -35778.713  
Iteration 5:   log likelihood = -35778.713  

ML fit of lognormal distribution                  Number of obs   =       4811
                                                  Wald chi2(0)    =          .
Log likelihood = -35778.713                       Prob > chi2     =          .

------------------------------------------------------------------------------
wnc_offer_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.386974    .003667  2014.46   0.000     7.379787    7.394161
-------------+----------------------------------------------------------------
v            |
       _cons |   .2543467   .0025929    98.09   0.000     .2492646    .2594288
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wnc_offer_beg_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  893.61533       0.00493
 5%   1.06e+03       0.02877
10%   1.17e+03       0.06228
20%   1.30e+03       0.13655
25%   1.36e+03       0.17649
30%   1.41e+03       0.21806
40%   1.51e+03       0.30583 Mode         1.51e+03
50%   1.61e+03       0.39961 Mean         1.67e+03
60%   1.72e+03       0.49960 Std. Dev.   431.17918
70%   1.85e+03       0.60644
75%   1.92e+03       0.66281 Variance     1.86e+05
80%   2.00e+03       0.72149 Half CV^2     0.03342
90%   2.24e+03       0.84784 Gini coeff.   0.14273
95%   2.45e+03       0.91781 p90/p10       1.91922
99%   2.92e+03       0.98087 p75/p25       1.40932
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_logn_9.pdf saved as PDF format
Distribution of wage offer after unemployment for cell 10

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wnc_offer_~y |       633        633   2175.249   492418.1   701.7251   2.233726   17.01559    1376933   500.4269   8211.471   847.7673   1326.205    1476.75   1730.637    2101.49   2487.757 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wnc_offer_~y |   2949.65   3219.411   4147.466 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_10.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_kdensity_10.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_qnorm_10.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                                  ----- Joint test -----
             Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
----------------------+-----------------------------------------------------------------
wnc_offer_beg_monthly |       633         0.0000         0.0000        273.53     0.0000

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -69609.865
rescale:       log likelihood = -6558.1168
rescale eq:    log likelihood =  -5262.196
Iteration 0:   log likelihood =  -5262.196  (not concave)
Iteration 1:   log likelihood = -5001.6481  
Iteration 2:   log likelihood = -4984.5449  
Iteration 3:   log likelihood = -4984.0847  
Iteration 4:   log likelihood = -4984.0834  
Iteration 5:   log likelihood = -4984.0834  

ML fit of lognormal distribution                  Number of obs   =        633
                                                  Wald chi2(0)    =          .
Log likelihood = -4984.0834                       Prob > chi2     =          .

------------------------------------------------------------------------------
wnc_offer_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.638503   .0121682   627.74   0.000     7.614654    7.662352
-------------+----------------------------------------------------------------
v            |
       _cons |   .3061464   .0086042    35.58   0.000     .2892824    .3230104
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wnc_offer_beg_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%   1.02e+03       0.00424
 5%   1.26e+03       0.02553
10%   1.40e+03       0.05618
20%   1.60e+03       0.12553
25%   1.69e+03       0.16339
30%   1.77e+03       0.20311
40%   1.92e+03       0.28791 Mode         1.89e+03
50%   2.08e+03       0.37975 Mean         2.18e+03
60%   2.24e+03       0.47895 Std. Dev.   682.17638
70%   2.44e+03       0.58638
75%   2.55e+03       0.64369 Variance     4.65e+05
80%   2.69e+03       0.70384 Half CV^2     0.04913
90%   3.07e+03       0.83532 Gini coeff.   0.17138
95%   3.44e+03       0.90967 p90/p10       2.19172
99%   4.23e+03       0.97832 p75/p25       1.51132
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_logn_10.pdf saved as PDF format
Distribution of wage offer after unemployment for cell 11

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wnc_offer_~y |      1357       1357   1728.647   422033.4   649.6409    2.49918   13.66346    2345774   290.1025   6398.343    662.973   1069.173   1188.037   1348.446   1601.308   1918.339 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wnc_offer_~y |   2393.52   2814.835    4371.11 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_11.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_kdensity_11.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_qnorm_11.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                                  ----- Joint test -----
             Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
----------------------+-----------------------------------------------------------------
wnc_offer_beg_monthly |     1,357         0.0000         0.0000        594.21     0.0000

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -139798.41
rescale:       log likelihood = -13662.273
rescale eq:    log likelihood = -11606.702
Iteration 0:   log likelihood = -11606.702  (not concave)
Iteration 1:   log likelihood = -10781.032  
Iteration 2:   log likelihood = -10490.125  
Iteration 3:   log likelihood =  -10466.29  
Iteration 4:   log likelihood =  -10466.28  
Iteration 5:   log likelihood =  -10466.28  

ML fit of lognormal distribution                  Number of obs   =       1357
                                                  Wald chi2(0)    =          .
Log likelihood =  -10466.28                       Prob > chi2     =          .

------------------------------------------------------------------------------
wnc_offer_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |    7.39842   .0089952   822.49   0.000     7.380789     7.41605
-------------+----------------------------------------------------------------
v            |
       _cons |   .3313599   .0063606    52.10   0.000     .3188934    .3438264
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wnc_offer_beg_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  755.63713       0.00393
 5%  947.07770       0.02407
10%   1.07e+03       0.05338
20%   1.24e+03       0.12040
25%   1.31e+03       0.15724
30%   1.37e+03       0.19607
40%   1.50e+03       0.27937 Mode         1.46e+03
50%   1.63e+03       0.37019 Mean         1.73e+03
60%   1.78e+03       0.46891 Std. Dev.   587.84903
70%   1.94e+03       0.57654
75%   2.04e+03       0.63425 Variance     3.46e+05
80%   2.16e+03       0.69507 Half CV^2     0.05803
90%   2.50e+03       0.82899 Gini coeff.   0.18525
95%   2.82e+03       0.90549 p90/p10       2.33803
99%   3.53e+03       0.97698 p75/p25       1.56361
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_logn_11.pdf saved as PDF format
Distribution of wage offer after unemployment for cell 12

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wnc_offer_~y |       270        270   2372.615    2302165   1517.289   7.363317   79.60341     640606    319.725   20319.14   349.0745   1383.208   1569.915   1841.552   2151.908   2480.341 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wnc_offer_~y |  3424.199    4119.38   8334.796 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_12.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_kdensity_12.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_qnorm_12.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                                  ----- Joint test -----
             Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
----------------------+-----------------------------------------------------------------
wnc_offer_beg_monthly |       270         0.0000         0.0000        289.51     0.0000

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -30000.812
rescale:       log likelihood =  -2808.783
rescale eq:    log likelihood = -2293.4612
Iteration 0:   log likelihood = -2293.4612  
Iteration 1:   log likelihood = -2290.3821  
Iteration 2:   log likelihood = -2231.5049  
Iteration 3:   log likelihood = -2230.3207  
Iteration 4:   log likelihood = -2230.3195  
Iteration 5:   log likelihood = -2230.3195  

ML fit of lognormal distribution                  Number of obs   =        270
                                                  Wald chi2(0)    =          .
Log likelihood = -2230.3195                       Prob > chi2     =          .

------------------------------------------------------------------------------
wnc_offer_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.670661   .0265595   288.81   0.000     7.618605    7.722716
-------------+----------------------------------------------------------------
v            |
       _cons |   .4364173   .0187804    23.24   0.000     .3996084    .4732262
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wnc_offer_beg_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  776.97029       0.00287
 5%   1.05e+03       0.01870
10%   1.23e+03       0.04290
20%   1.49e+03       0.10062
25%   1.60e+03       0.13330
30%   1.71e+03       0.16832
40%   1.92e+03       0.24517 Mode         1.77e+03
50%   2.14e+03       0.33127 Mean         2.36e+03
60%   2.40e+03       0.42737 Std. Dev.    1.08e+03
70%   2.70e+03       0.53505
75%   2.88e+03       0.59409 Variance     1.17e+06
80%   3.10e+03       0.65734 Half CV^2     0.10490
90%   3.75e+03       0.80098 Gini coeff.   0.24237
95%   4.40e+03       0.88656 p90/p10       3.06051
99%   5.92e+03       0.97062 p75/p25       1.80168
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_logn_12.pdf saved as PDF format
Distribution of wage offer after unemployment for cell 13

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   1180.466   108791.2   329.8351  -.3714625   3.592671   44857.69   295.0202   2027.411   295.0202   617.0993   692.6213   968.3619   1253.373   1395.493 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wnc_offer_~y |  1524.373   1571.624   2027.411 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_13.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_kdensity_13.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_qnorm_13.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                                  ----- Joint test -----
             Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
----------------------+-----------------------------------------------------------------
wnc_offer_beg_monthly |        NA         0.2950         0.2286          2.74     0.2547

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -3519.5868
rescale:       log likelihood =  -365.5281
rescale eq:    log likelihood = -322.15824
Iteration 0:   log likelihood = -322.15824  (not concave)
Iteration 1:   log likelihood = -295.90808  (not concave)
Iteration 2:   log likelihood = -286.29383  
Iteration 3:   log likelihood = -281.39829  
Iteration 4:   log likelihood =  -280.1226  
Iteration 5:   log likelihood = -280.11775  
Iteration 6:   log likelihood = -280.11774  

ML fit of lognormal distribution                  Number of obs   =         NA
                                                  Wald chi2(0)    =          .
Log likelihood = -280.11774                       Prob > chi2     =          .

------------------------------------------------------------------------------
wnc_offer_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.024418   .0555412   126.47   0.000     6.915559    7.133276
-------------+----------------------------------------------------------------
v            |
       _cons |   .3423791   .0392736     8.72   0.000     .2654043    .4193539
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wnc_offer_beg_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  506.70281       0.00381
 5%  639.86297       0.02345
10%  724.61544       0.05220
20%  842.40766       0.11821
25%  892.01807       0.15461
30%  939.05477       0.19303
40%   1.03e+03       0.27568 Mode        999.43901
50%   1.12e+03       0.36603 Mean         1.19e+03
60%   1.23e+03       0.46453 Std. Dev.   420.22271
70%   1.34e+03       0.57222
75%   1.42e+03       0.63010 Variance     1.77e+05
80%   1.50e+03       0.69120 Half CV^2     0.06219
90%   1.74e+03       0.82618 Gini coeff.   0.19130
95%   1.97e+03       0.90362 p90/p10       2.40501
99%   2.49e+03       0.97637 p75/p25       1.58703
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_logn_13.pdf saved as PDF format
Distribution of wage offer after unemployment for cell 14

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wnc_offer_~y |         NA         NA  1749.318     304998   552.2663   1.286568   2.972563   8746.592   1333.346   2708.138   1333.346   1333.346   1333.346   1442.389    1578.49   1684.229 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wnc_offer_~y |  2708.138   2708.138   2708.138 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_14.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_kdensity_14.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_qnorm_14.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                                  ----- Joint test -----
             Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
----------------------+-----------------------------------------------------------------
wnc_offer_beg_monthly |         NA            .              .              .          .

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -519.62735
rescale:       log likelihood = -50.544476
rescale eq:    log likelihood = -42.123149
Iteration 0:   log likelihood = -42.123149  (not concave)
Iteration 1:   log likelihood = -38.062739  
Iteration 2:   log likelihood = -37.373811  
Iteration 3:   log likelihood =  -37.30845  
Iteration 4:   log likelihood = -37.296172  
Iteration 5:   log likelihood = -37.296168  
Iteration 6:   log likelihood = -37.296168  

ML fit of lognormal distribution                  Number of obs   =         NA
                                                  Wald chi2(0)    =          .
Log likelihood = -37.296168                       Prob > chi2     =          .

------------------------------------------------------------------------------
wnc_offer_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.433361   .1110489    66.94   0.000      7.21571    7.651013
-------------+----------------------------------------------------------------
v            |
       _cons |   .2483128   .0785234     3.16   0.002     .0944098    .4022158
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wnc_offer_beg_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  949.27585       0.00502
 5%   1.12e+03       0.02917
10%   1.23e+03       0.06303
20%   1.37e+03       0.13787
25%   1.43e+03       0.17806
30%   1.48e+03       0.21985
40%   1.59e+03       0.30795 Mode         1.59e+03
50%   1.69e+03       0.40195 Mean         1.74e+03
60%   1.80e+03       0.50201 Std. Dev.   439.93145
70%   1.93e+03       0.60876
75%   2.00e+03       0.66501 Variance     1.94e+05
80%   2.08e+03       0.72351 Half CV^2     0.03180
90%   2.33e+03       0.84925 Gini coeff.   0.13938
95%   2.54e+03       0.91872 p90/p10       1.88976
99%   3.01e+03       0.98115 p75/p25       1.39790
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_logn_14.pdf saved as PDF format
Distribution of wage offer after unemployment for cell 15

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wnc_offer_~y |       199        199   1210.725   108011.1   328.6504    -.23635   3.188727   240934.3   335.8101   2258.389      427.7   569.9305   726.2833   1012.737   1259.036   1406.803 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wnc_offer_~y |  1584.144   1706.514   2016.155 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_15.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_kdensity_15.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_qnorm_15.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                                  ----- Joint test -----
             Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
----------------------+-----------------------------------------------------------------
wnc_offer_beg_monthly |       199         0.1641         0.4443          2.55     0.2796

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood =  -18589.74
rescale:       log likelihood = -1921.3559
rescale eq:    log likelihood = -1685.4678
Iteration 0:   log likelihood = -1685.4678  (not concave)
Iteration 1:   log likelihood = -1541.2683  (not concave)
Iteration 2:   log likelihood = -1497.4758  
Iteration 3:   log likelihood = -1484.9394  
Iteration 4:   log likelihood = -1455.9011  
Iteration 5:   log likelihood = -1455.6825  
Iteration 6:   log likelihood = -1455.6817  
Iteration 7:   log likelihood = -1455.6817  

ML fit of lognormal distribution                  Number of obs   =        199
                                                  Wald chi2(0)    =          .
Log likelihood = -1455.6817                       Prob > chi2     =          .

------------------------------------------------------------------------------
wnc_offer_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.055074    .022244   317.17   0.000     7.011476    7.098671
-------------+----------------------------------------------------------------
v            |
       _cons |   .3137908   .0157289    19.95   0.000     .2829627    .3446189
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wnc_offer_beg_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  558.40650       0.00414
 5%  691.54888       0.02508
10%  775.05553       0.05532
20%  889.78581       0.12396
25%  937.69534       0.16151
30%  982.91409       0.20096
40%   1.07e+03       0.28531 Mode         1.05e+03
50%   1.16e+03       0.37684 Mean         1.22e+03
60%   1.25e+03       0.47590 Std. Dev.   391.54308
70%   1.37e+03       0.58340
75%   1.43e+03       0.64084 Variance     1.53e+05
80%   1.51e+03       0.70119 Half CV^2     0.05174
90%   1.73e+03       0.83342 Gini coeff.   0.17560
95%   1.94e+03       0.90842 p90/p10       2.23508
99%   2.40e+03       0.97792 p75/p25       1.52699
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_logn_15.pdf saved as PDF format
Distribution of wage offer after unemployment for cell 16

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   1497.174   251736.8   501.7338   .0380096   1.979537   34434.99   659.9534   2440.824   659.9534   779.1863   791.9146    1138.04   1429.638   1926.012 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wnc_offer_~y |  2090.082   2195.383   2440.824 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_16.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_kdensity_16.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_qnorm_16.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                                  ----- Joint test -----
             Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
----------------------+-----------------------------------------------------------------
wnc_offer_beg_monthly |        NA         0.9283         0.1871          1.93     0.3808

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -2274.6055
rescale:       log likelihood = -227.49267
rescale eq:    log likelihood = -195.83548
Iteration 0:   log likelihood = -195.83548  (not concave)
Iteration 1:   log likelihood = -176.94246  
Iteration 2:   log likelihood = -175.79292  
Iteration 3:   log likelihood = -175.78553  
Iteration 4:   log likelihood = -175.78553  

ML fit of lognormal distribution                  Number of obs   =         NA
                                                  Wald chi2(0)    =          .
Log likelihood = -175.78553                       Prob > chi2     =          .

------------------------------------------------------------------------------
wnc_offer_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.251389    .074629    97.17   0.000     7.105119     7.39766
-------------+----------------------------------------------------------------
v            |
       _cons |   .3579082   .0527707     6.78   0.000     .2544796    .4613368
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wnc_offer_beg_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  613.24855       0.00363
 5%  782.64775       0.02260
10%  891.32714       0.05056
20%   1.04e+03       0.11516
25%   1.11e+03       0.15094
30%   1.17e+03       0.18880
40%   1.29e+03       0.27052 Mode         1.24e+03
50%   1.41e+03       0.36021 Mean         1.50e+03
60%   1.54e+03       0.45836 Std. Dev.   555.75426
70%   1.70e+03       0.56612
75%   1.80e+03       0.62422 Variance     3.09e+05
80%   1.91e+03       0.68571 Half CV^2     0.06833
90%   2.23e+03       0.82216 Gini coeff.   0.19979
95%   2.54e+03       0.90094 p90/p10       2.50266
99%   3.24e+03       0.97549 p75/p25       1.62062
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_logn_16.pdf saved as PDF format
Distribution of wage offer after unemployment for cell 17

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   1228.936   220549.9   469.6274   2.407208   16.72959   117977.9   279.8522   4090.868   279.8522   545.0964   681.4068     1028.8   1233.138   1420.535 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wnc_offer_~y |  1584.927    1620.99   4090.868 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_17.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_kdensity_17.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_qnorm_17.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                                  ----- Joint test -----
             Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
----------------------+-----------------------------------------------------------------
wnc_offer_beg_monthly |        NA         0.0000         0.0000         55.51     0.0000

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -8952.6973
rescale:       log likelihood = -925.94294
rescale eq:    log likelihood =  -815.4972
Iteration 0:   log likelihood =  -815.4972  (not concave)
Iteration 1:   log likelihood = -750.66764  (not concave)
Iteration 2:   log likelihood =  -725.5664  
Iteration 3:   log likelihood = -721.81669  
Iteration 4:   log likelihood = -721.38861  
Iteration 5:   log likelihood = -721.38821  
Iteration 6:   log likelihood = -721.38821  

ML fit of lognormal distribution                  Number of obs   =         NA
                                                  Wald chi2(0)    =          .
Log likelihood = -721.38821                       Prob > chi2     =          .

------------------------------------------------------------------------------
wnc_offer_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.045488   .0394729   178.49   0.000     6.968123    7.122854
-------------+----------------------------------------------------------------
v            |
       _cons |   .3867542   .0279116    13.86   0.000     .3320485    .4414599
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wnc_offer_beg_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  466.73575       0.00333
 5%  607.48896       0.02110
10%  699.13412       0.04763
20%  828.80715       0.10965
25%  884.14961       0.14429
30%  936.99124       0.18111
40%   1.04e+03       0.26105 Mode        988.22384
50%   1.15e+03       0.34947 Mean         1.24e+03
60%   1.27e+03       0.44694 Std. Dev.   496.79238
70%   1.41e+03       0.55474
75%   1.49e+03       0.61323 Variance     2.47e+05
80%   1.59e+03       0.67540 Half CV^2     0.08067
90%   1.88e+03       0.81455 Gini coeff.   0.21551
95%   2.17e+03       0.89582 p90/p10       2.69471
99%   2.82e+03       0.97379 p75/p25       1.68493
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_logn_17.pdf saved as PDF format
Distribution of wage offer after unemployment for cell 18

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   1345.177   204673.2   452.4083   .2602137   2.020657   16142.12   625.1046   2054.858   625.1046   625.1046   931.5843   1013.956   1277.324   1725.035 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wnc_offer_~y |  2030.947   2054.858   2054.858 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_18.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_kdensity_18.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_qnorm_18.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                                  ----- Joint test -----
             Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
----------------------+-----------------------------------------------------------------
wnc_offer_beg_monthly |        NA         0.6241         0.5256          0.69     0.7097

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -1152.3919
rescale:       log likelihood = -117.21764
rescale eq:    log likelihood = -101.84914
Iteration 0:   log likelihood = -101.84914  (not concave)
Iteration 1:   log likelihood =  -92.52957  
Iteration 2:   log likelihood =  -89.86736  (backed up)
Iteration 3:   log likelihood = -89.825435  
Iteration 4:   log likelihood = -89.825398  
Iteration 5:   log likelihood = -89.825398  

ML fit of lognormal distribution                  Number of obs   =         NA
                                                  Wald chi2(0)    =          .
Log likelihood = -89.825398                       Prob > chi2     =          .

------------------------------------------------------------------------------
wnc_offer_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.149367   .0977533    73.14   0.000     6.957774     7.34096
-------------+----------------------------------------------------------------
v            |
       _cons |   .3386272    .069122     4.90   0.000     .2031506    .4741038
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wnc_offer_beg_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  579.17363       0.00385
 5%  729.51126       0.02366
10%  825.01268       0.05260
20%  957.54348       0.11895
25%   1.01e+03       0.15550
30%   1.07e+03       0.19406
40%   1.17e+03       0.27693 Mode         1.14e+03
50%   1.27e+03       0.36745 Mean         1.35e+03
60%   1.39e+03       0.46602 Std. Dev.   470.02537
70%   1.52e+03       0.57369
75%   1.60e+03       0.63151 Variance     2.21e+05
80%   1.69e+03       0.69252 Half CV^2     0.06075
90%   1.97e+03       0.82714 Gini coeff.   0.18924
95%   2.22e+03       0.90426 p90/p10       2.38199
99%   2.80e+03       0.97658 p75/p25       1.57901
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_logn_18.pdf saved as PDF format
Distribution of wage offer after unemployment for cell 19

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wnc_offer_~y |       775        775    1315.96   150044.3   387.3555   1.163745    7.93411    1019869    330.172   3813.059   513.2851   722.3479    846.896   1104.974   1298.453   1499.443 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wnc_offer_~y |  1763.458   1950.647   2489.398 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_19.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_kdensity_19.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_qnorm_19.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                                  ----- Joint test -----
             Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
----------------------+-----------------------------------------------------------------
wnc_offer_beg_monthly |       775         0.0000         0.0000        160.51     0.0000

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -74172.297
rescale:       log likelihood = -7560.3987
rescale eq:    log likelihood =  -6566.996
Iteration 0:   log likelihood =  -6566.996  (not concave)
Iteration 1:   log likelihood = -5944.5855  (not concave)
Iteration 2:   log likelihood = -5767.7577  
Iteration 3:   log likelihood =  -5720.013  
Iteration 4:   log likelihood = -5698.8062  
Iteration 5:   log likelihood = -5698.7009  
Iteration 6:   log likelihood = -5698.7009  

ML fit of lognormal distribution                  Number of obs   =        775
                                                  Wald chi2(0)    =          .
Log likelihood = -5698.7009                       Prob > chi2     =          .

------------------------------------------------------------------------------
wnc_offer_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.139333   .0107641   663.25   0.000     7.118235     7.16043
-------------+----------------------------------------------------------------
v            |
       _cons |   .2996594   .0076114    39.37   0.000     .2847415    .3145774
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wnc_offer_beg_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  627.79926       0.00432
 5%  770.03552       0.02592
10%  858.60033       0.05691
20%  979.58874       0.12688
25%   1.03e+03       0.16499
30%   1.08e+03       0.20495
40%   1.17e+03       0.29013 Mode         1.15e+03
50%   1.26e+03       0.38222 Mean         1.32e+03
60%   1.36e+03       0.48153 Std. Dev.   404.13087
70%   1.48e+03       0.58891
75%   1.54e+03       0.64611 Variance     1.63e+05
80%   1.62e+03       0.70608 Half CV^2     0.04698
90%   1.85e+03       0.83692 Gini coeff.   0.16781
95%   2.06e+03       0.91072 p90/p10       2.15558
99%   2.53e+03       0.97865 p75/p25       1.49816
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_logn_19.pdf saved as PDF format
Distribution of wage offer after unemployment for cell 20

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   1721.614   403627.6   635.3169   1.248076   6.942183   86080.68   527.4063   4311.098   527.4063   772.9308   987.4135   1323.732   1650.748   2027.691 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wnc_offer_~y |  2376.973   2536.792   4311.098 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_20.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_kdensity_20.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_qnorm_20.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                                  ----- Joint test -----
             Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
----------------------+-----------------------------------------------------------------
wnc_offer_beg_monthly |        NA         0.0008         0.0009         16.98     0.0002

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -5134.9214
rescale:       log likelihood = -502.64073
rescale eq:    log likelihood = -428.11507
Iteration 0:   log likelihood = -428.11507  (not concave)
Iteration 1:   log likelihood = -399.52152  
Iteration 2:   log likelihood = -390.87554  
Iteration 3:   log likelihood =  -390.7612  
Iteration 4:   log likelihood = -390.76109  
Iteration 5:   log likelihood = -390.76109  

ML fit of lognormal distribution                  Number of obs   =         NA
                                                  Wald chi2(0)    =          .
Log likelihood = -390.76109                       Prob > chi2     =          .

------------------------------------------------------------------------------
wnc_offer_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.385155   .0526082   140.38   0.000     7.282045    7.488265
-------------+----------------------------------------------------------------
v            |
       _cons |   .3719962   .0371996    10.00   0.000     .2990863    .4449061
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wnc_offer_beg_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  678.41721       0.00348
 5%  874.17075       0.02186
10%   1.00e+03       0.04911
20%   1.18e+03       0.11245
25%   1.25e+03       0.14767
30%   1.33e+03       0.18502
40%   1.47e+03       0.26587 Mode         1.40e+03
50%   1.61e+03       0.35495 Mean         1.73e+03
60%   1.77e+03       0.45278 Std. Dev.   665.45312
70%   1.96e+03       0.56057
75%   2.07e+03       0.61886 Variance     4.43e+05
80%   2.20e+03       0.68069 Half CV^2     0.07421
90%   2.60e+03       0.81847 Gini coeff.   0.20748
95%   2.97e+03       0.89847 p90/p10       2.59468
99%   3.83e+03       0.97467 p75/p25       1.65172
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_logn_20.pdf saved as PDF format
Distribution of wage offer after unemployment for cell 21

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wnc_offer_~y |      2959       2959   1366.717   156945.9    396.164   .9172204    6.61544    4044115   266.8943   4247.848   530.2181   745.9658   887.4824   1147.571   1344.903   1555.199 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wnc_offer_~y |  1820.371   2063.993   2587.605 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_21.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_kdensity_21.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_qnorm_21.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                                  ----- Joint test -----
             Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
----------------------+-----------------------------------------------------------------
wnc_offer_beg_monthly |     2,959         0.0000         0.0000        446.88     0.0000

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -286289.02
rescale:       log likelihood = -28999.896
rescale eq:    log likelihood = -25093.408
Iteration 0:   log likelihood = -25093.408  (not concave)
Iteration 1:   log likelihood = -22616.774  (not concave)
Iteration 2:   log likelihood = -21980.256  
Iteration 3:   log likelihood = -21899.192  
Iteration 4:   log likelihood = -21897.608  
Iteration 5:   log likelihood = -21897.604  
Iteration 6:   log likelihood = -21897.604  

ML fit of lognormal distribution                  Number of obs   =       2959
                                                  Wald chi2(0)    =          .
Log likelihood = -21897.604                       Prob > chi2     =          .

------------------------------------------------------------------------------
wnc_offer_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.177053   .0055611  1290.58   0.000     7.166154    7.187953
-------------+----------------------------------------------------------------
v            |
       _cons |   .3025065   .0039323    76.93   0.000     .2947993    .3102136
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wnc_offer_beg_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  647.62912       0.00428
 5%  795.90083       0.02575
10%  888.35888       0.05659
20%   1.01e+03       0.12629
25%   1.07e+03       0.16429
30%   1.12e+03       0.20414
40%   1.21e+03       0.28916 Mode         1.19e+03
50%   1.31e+03       0.38113 Mean         1.37e+03
60%   1.41e+03       0.48040 Std. Dev.   424.20118
70%   1.53e+03       0.58780
75%   1.61e+03       0.64505 Variance     1.80e+05
80%   1.69e+03       0.70510 Half CV^2     0.04791
90%   1.93e+03       0.83622 Gini coeff.   0.16938
95%   2.15e+03       0.91026 p90/p10       2.17136
99%   2.65e+03       0.97851 p75/p25       1.50392
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_logn_21.pdf saved as PDF format
Distribution of wage offer after unemployment for cell 22

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wnc_offer_~y |       141        141   1619.193   364063.6   603.3768   .9288857   5.178134   228306.2   340.2527   4308.878   359.2049   804.9374    917.304   1216.227   1522.139   1964.039 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wnc_offer_~y |   2433.96   2542.946   3343.501 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_22.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_kdensity_22.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_qnorm_22.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                                  ----- Joint test -----
             Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
----------------------+-----------------------------------------------------------------
wnc_offer_beg_monthly |       141         0.0000         0.0009         21.84     0.0000

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -14217.319
rescale:       log likelihood = -1406.1262
rescale eq:    log likelihood = -1205.0493
Iteration 0:   log likelihood = -1205.0493  (not concave)
Iteration 1:   log likelihood = -1101.7347  
Iteration 2:   log likelihood = -1099.9289  
Iteration 3:   log likelihood = -1099.9124  
Iteration 4:   log likelihood = -1099.9124  

ML fit of lognormal distribution                  Number of obs   =        141
                                                  Wald chi2(0)    =          .
Log likelihood = -1099.9124                       Prob > chi2     =          .

------------------------------------------------------------------------------
wnc_offer_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.318378   .0330115   221.69   0.000     7.253677     7.38308
-------------+----------------------------------------------------------------
v            |
       _cons |   .3919896   .0233426    16.79   0.000     .3462389    .4377404
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wnc_offer_beg_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  605.75407       0.00328
 5%  791.24904       0.02083
10%  912.34974       0.04711
20%   1.08e+03       0.10867
25%   1.16e+03       0.14310
30%   1.23e+03       0.17973
40%   1.37e+03       0.25935 Mode         1.29e+03
50%   1.51e+03       0.34753 Mean         1.63e+03
60%   1.67e+03       0.44487 Std. Dev.   663.54198
70%   1.85e+03       0.55267
75%   1.96e+03       0.61122 Variance     4.40e+05
80%   2.10e+03       0.67351 Half CV^2     0.08304
90%   2.49e+03       0.81315 Gini coeff.   0.21836
95%   2.87e+03       0.89487 p90/p10       2.73111
99%   3.75e+03       0.97347 p75/p25       1.69687
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_logn_22.pdf saved as PDF format
Distribution of wage offer after unemployment for cell 23

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wnc_offer_~y |      1033       1033   1355.259   174044.8   417.1867   .8279905   5.376765    1399982   257.8651   3406.287   474.1325   703.2082   854.4485   1131.922   1332.509   1531.126 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wnc_offer_~y |  1858.711   2062.719   2721.934 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_23.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_kdensity_23.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_qnorm_23.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                                  ----- Joint test -----
             Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
----------------------+-----------------------------------------------------------------
wnc_offer_beg_monthly |     1,033         0.0000         0.0000        121.30     0.0000

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood = -99556.117
rescale:       log likelihood = -10106.349
rescale eq:    log likelihood = -8765.0701
Iteration 0:   log likelihood = -8765.0701  (not concave)
Iteration 1:   log likelihood = -7938.1894  
Iteration 2:   log likelihood = -7732.4864  (backed up)
Iteration 3:   log likelihood = -7717.7614  
Iteration 4:   log likelihood = -7704.8045  
Iteration 5:   log likelihood = -7704.7121  
Iteration 6:   log likelihood = -7704.7121  

ML fit of lognormal distribution                  Number of obs   =       1033
                                                  Wald chi2(0)    =          .
Log likelihood = -7704.7121                       Prob > chi2     =          .

------------------------------------------------------------------------------
wnc_offer_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.162423   .0101235   707.50   0.000     7.142582    7.182265
-------------+----------------------------------------------------------------
v            |
       _cons |   .3253731   .0071584    45.45   0.000     .3113429    .3394033
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wnc_offer_beg_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  605.15963       0.00400
 5%  755.38849       0.02441
10%  850.17387       0.05404
20%  981.00973       0.12161
25%   1.04e+03       0.15869
30%   1.09e+03       0.19773
40%   1.19e+03       0.28139 Mode         1.16e+03
50%   1.29e+03       0.37245 Mean         1.36e+03
60%   1.40e+03       0.47129 Std. Dev.   454.53491
70%   1.53e+03       0.57888
75%   1.61e+03       0.63650 Variance     2.07e+05
80%   1.70e+03       0.69716 Half CV^2     0.05584
90%   1.96e+03       0.83051 Gini coeff.   0.18197
95%   2.20e+03       0.90650 p90/p10       2.30243
99%   2.75e+03       0.97730 p75/p25       1.55103
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_logn_23.pdf saved as PDF format
Distribution of wage offer after unemployment for cell 24

             |  e(count)   e(sum_w)    e(mean)     e(Var)      e(sd)  e(skewn~)  e(kurto~)     e(sum)     e(min)     e(max)      e(p1)      e(p5)     e(p10)     e(p25)     e(p50)     e(p75) 
-------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
wnc_offer_~y |        NA         NA   1664.712   466388.9   682.9267   .7570469   3.917121   89894.47   382.1614   3795.282   382.1614    585.439    878.237   1193.839   1685.366   1907.231 

             |    e(p90)     e(p95)     e(p99) 
-------------+---------------------------------
wnc_offer_~y |  2523.726   3019.218   3795.282 
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_24.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_kdensity_24.pdf saved as PDF format
file results/two/5_sample2_wnc_offer_qnorm_24.pdf saved as PDF format

Skewness and kurtosis tests for normality
                                                                  ----- Joint test -----
             Variable |       Obs   Pr(skewness)   Pr(kurtosis)   Adj chi2(2)  Prob>chi2
----------------------+-----------------------------------------------------------------
wnc_offer_beg_monthly |        NA         0.0205         0.1121          7.10     0.0287

initial:       log likelihood =     -<inf>  (could not be evaluated)
feasible:      log likelihood =  -5464.971
rescale:       log likelihood = -539.25142
rescale eq:    log likelihood = -462.79914
Iteration 0:   log likelihood = -462.79914  (not concave)
Iteration 1:   log likelihood = -430.19421  
Iteration 2:   log likelihood =  -428.2195  
Iteration 3:   log likelihood =  -428.2091  
Iteration 4:   log likelihood = -428.20909  

ML fit of lognormal distribution                  Number of obs   =         NA
                                                  Wald chi2(0)    =          .
Log likelihood = -428.20909                       Prob > chi2     =          .

------------------------------------------------------------------------------
wnc_offer_~y | Coefficient  Std. err.      z    P>|z|     [95% conf. interval]
-------------+----------------------------------------------------------------
m            |
       _cons |   7.328582   .0600718   122.00   0.000     7.210843    7.446321
-------------+----------------------------------------------------------------
v            |
       _cons |   .4414357   .0424771    10.39   0.000     .3581821    .5246894
------------------------------------------------------------------------------
------------------------------------------------------------
     Quantiles       Cumulative shares of
                     total wnc_offer_beg_monthly (Lorenz ordinates)
------------------------------------------------------------
 1%  545.47058       0.00282
 5%  736.92401       0.01848
10%  865.11228       0.04245
20%   1.05e+03       0.09974
25%   1.13e+03       0.13223
30%   1.21e+03       0.16706
40%   1.36e+03       0.24360 Mode         1.25e+03
50%   1.52e+03       0.32945 Mean         1.68e+03
60%   1.70e+03       0.42540 Std. Dev.   778.83492
70%   1.92e+03       0.53306
75%   2.05e+03       0.59214 Variance     6.07e+05
80%   2.21e+03       0.65549 Half CV^2     0.10757
90%   2.68e+03       0.79958 Gini coeff.   0.24507
95%   3.15e+03       0.88559 p90/p10       3.10013
99%   4.25e+03       0.97028 p75/p25       1.81392
------------------------------------------------------------
file results/two/5_sample2_wages_distribution.xlsx saved
file results/two/5_sample2_wnc_offer_dpplot_logn_24.pdf saved as PDF format
r; t=1271.59 15:55:26

. 
.         
. * (9.3) We then assume w' is a function of wc and estimate for each cell
. * a simple linear relationship using different specifications: 
. * - reg w' wc  i.year
. * - reg w' wc  i.decades
. * - reg w' wc year
. * - reg w* wc decades
. 
. * Nota bene: A spell can inform us on wcoal and woffer, if it is both a post-unemp
. *                       and a pre-unemp spell. NB. even then, tentg_end and tentg_beg may differ
. *
. 
. di "Number of spells that inform us on both last wcoal and first woffer"
Number of spells that inform us on both last wcoal and first woffer
r; t=0.00 15:55:26

. count if wcoal_end_monthly!=. & wnc_offer_beg_monthly!=.
  0
r; t=0.04 15:55:26

. 
. sort pid begepi
r; t=0.21 15:55:26

. replace wnc_offer_beg_monthly = wnc_offer_beg_monthly[_n+2] if pretrans == 7 & pid == pid[_n+2]
(15,383 real changes made)
r; t=0.32 15:55:27

. 
. * full sample
. capture noisily reg wnc_offer_beg_monthly wcoal_end_monthly  i.jahrend, robust

Linear regression                               Number of obs     =     15,383
                                                F(41, 15340)      =          .
                                                Prob > F          =          .
                                                R-squared         =     0.1937
                                                Root MSE          =     503.78

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .2909422   .0230148    12.64   0.000     .2458305     .336054
                  |
          jahrend |
            1977  |    430.494   211.9749     2.03   0.042     14.99812    845.9899
            1978  |  -59.05058   220.6982    -0.27   0.789    -491.6452    373.5441
            1979  |   158.0633   127.7875     1.24   0.216    -92.41538     408.542
            1980  |   139.5255   83.44099     1.67   0.095    -24.02878    303.0797
            1981  |   198.1406    115.674     1.71   0.087    -28.59424    424.8754
            1982  |  -49.14437   33.22842    -1.48   0.139     -114.276    15.98727
            1983  |   -23.9306    54.4187    -0.44   0.660    -130.5977    82.73651
            1984  |   140.6773   108.3413     1.30   0.194    -71.68441     353.039
            1985  |   92.79815   102.6061     0.90   0.366     -108.322    293.9183
            1986  |   116.1865   103.7263     1.12   0.263    -87.12939    319.5025
            1987  |   237.0294   92.02357     2.58   0.010      56.6523    417.4065
            1988  |   383.8539   140.2513     2.74   0.006     108.9448    658.7631
            1989  |   363.1724   111.4487     3.26   0.001     144.7197    581.6251
            1990  |   244.9794   149.1752     1.64   0.101    -47.42171    537.3804
            1991  |   160.6918   107.0038     1.50   0.133     -49.0483     370.432
            1992  |  -351.8201   29.43141   -11.95   0.000    -409.5092   -294.1311
            1993  |  -416.9364   19.28805   -21.62   0.000    -454.7433   -379.1296
            1994  |  -474.6712   20.33601   -23.34   0.000    -514.5322   -434.8102
            1995  |  -326.3855   24.64243   -13.24   0.000    -374.6875   -278.0834
            1996  |  -285.0944   29.04025    -9.82   0.000    -342.0167    -228.172
            1997  |  -311.5912   45.20345    -6.89   0.000    -400.1953    -222.987
            1998  |   -331.383   71.19757    -4.65   0.000    -470.9387   -191.8273
            1999  |  -402.9472   72.55543    -5.55   0.000    -545.1645     -260.73
            2000  |  -269.2526   72.81595    -3.70   0.000    -411.9805   -126.5247
            2001  |   -378.281   154.8903    -2.44   0.015    -681.8843   -74.67767
            2002  |  -173.5203   98.46277    -1.76   0.078     -366.519     19.4784
            2003  |  -145.0118   106.7102    -1.36   0.174    -354.1764     64.1528
            2004  |  -134.3871   301.0414    -0.45   0.655    -724.4639    455.6897
            2005  |  -501.3972   144.6675    -3.47   0.001    -784.9626   -217.8318
            2006  |  -407.4889   199.5661    -2.04   0.041    -798.6621   -16.31568
            2007  |  -159.9373   266.6161    -0.60   0.549    -682.5365    362.6618
            2008  |  -114.4645   308.1778    -0.37   0.710    -718.5294    489.6005
            2009  |  -566.4925    121.936    -4.65   0.000    -805.5015   -327.4836
            2010  |  -45.65778   516.8736    -0.09   0.930    -1058.791    967.4758
            2011  |  -457.4018   168.2742    -2.72   0.007    -787.2392   -127.5643
            2012  |   -340.882    310.779    -1.10   0.273    -950.0458    268.2817
            2013  |   55.44111   583.7596     0.09   0.924    -1088.797    1199.679
            2014  |   212.9599   315.8767     0.67   0.500    -406.1959    832.1157
            2015  |   1981.071   1727.139     1.15   0.251    -1404.326    5366.469
            2016  |   1001.224   625.9665     1.60   0.110    -225.7445    2228.193
            2017  |   -205.799   526.1529    -0.39   0.696    -1237.121    825.5231
                  |
            _cons |   1304.657    72.0838    18.10   0.000     1163.364    1445.949
-----------------------------------------------------------------------------------
r; t=0.37 15:55:27

. if _rc!=2000 & _rc!=2001 & ${iab}==1{
.         matrix output1=r(table)
r; t=0.00 15:55:27
.         mat reg_ijahrend=output1'
r; t=0.00 15:55:27
.         matrix list reg_ijahrend

reg_ijahrend[44,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .29094225   .02301482   12.641517   1.894e-36   .24583047   .33605403       15340   1.9601186           0
1976b.jahr~d           0           .           .           .           .           .       15340   1.9601186           0
1977.jahrend   430.49401   211.97487   2.0308729    .0422851   14.998124    845.9899       15340   1.9601186           0
1978.jahrend  -59.050583    220.6982   -.2675626   .78903962  -491.64523   373.54407       15340   1.9601186           0
1979.jahrend   158.06331   127.78752   1.2369229   .21613464  -92.415385     408.542       15340   1.9601186           0
1980.jahrend   139.52546   83.440993   1.6721453   .09451607  -24.028782   303.07971       15340   1.9601186           0
1981.jahrend   198.14056   115.67402   1.7129219   .08674711  -28.594237   424.87535       15340   1.9601186           0
1982.jahrend  -49.144367   33.228417  -1.4789861   .13916456  -114.27601   15.987272       15340   1.9601186           0
1983.jahrend  -23.930596   54.418697  -.43974951   .66012473   -130.5977   82.736508       15340   1.9601186           0
1984.jahrend    140.6773   108.34125   1.2984648   .19414719  -71.684409   353.03902       15340   1.9601186           0
1985.jahrend   92.798153   102.60611    .9044116   .36579139  -108.32199    293.9183       15340   1.9601186           0
1986.jahrend   116.18654   103.72634   1.1201258   .26267771  -87.129386   319.50248       15340   1.9601186           0
1987.jahrend   237.02941   92.023567   2.5757468   .01001166   56.652296   417.40651       15340   1.9601186           0
1988.jahrend   383.85394   140.25128   2.7369015   .00620923    108.9448   658.76309       15340   1.9601186           0
1989.jahrend   363.17237   111.44872     3.25865   .00112187   144.71965   581.62509       15340   1.9601186           0
1990.jahrend   244.97936   149.17519   1.6422259   .10056369  -47.421709   537.38043       15340   1.9601186           0
1991.jahrend   160.69185    107.0038   1.5017396   .13318496  -49.048304   370.43199       15340   1.9601186           0
1992.jahrend  -351.82011   29.431407  -11.953901   8.663e-33  -409.50916  -294.13106       15340   1.9601186           0
1993.jahrend  -416.93643   19.288052  -21.616306   4.19e-102   -454.7433  -379.12956       15340   1.9601186           0
1994.jahrend  -474.67118   20.336008  -23.341413   1.93e-118  -514.53217  -434.81019       15340   1.9601186           0
1995.jahrend  -326.38546   24.642429  -13.244857   7.994e-40  -374.68755  -278.08338       15340   1.9601186           0
1996.jahrend  -285.09437   29.040247  -9.8172155   1.107e-22   -342.0167  -228.17204       15340   1.9601186           0
1997.jahrend  -311.59116   45.203448   -6.893084   5.672e-12  -400.19528  -222.98704       15340   1.9601186           0
1998.jahrend  -331.38301   71.197566  -4.6544149   3.276e-06  -470.93869  -191.82734       15340   1.9601186           0
1999.jahrend  -402.94723    72.55543  -5.5536468   2.844e-08  -545.16448  -260.72998       15340   1.9601186           0
2000.jahrend  -269.25264   72.815951  -3.6977151   .00021831  -411.98054  -126.52474       15340   1.9601186           0
2001.jahrend  -378.28098   154.89027  -2.4422514   .01460719  -681.88429  -74.677671       15340   1.9601186           0
2002.jahrend   -173.5203   98.462765  -1.7622936   .07803961  -366.51901   19.478397       15340   1.9601186           0
2003.jahrend  -145.01181   106.71018  -1.3589314    .1741883  -354.17642   64.152796       15340   1.9601186           0
2004.jahrend  -134.38709   301.04139  -.44640737   .65530931  -724.46393   455.68974       15340   1.9601186           0
2005.jahrend  -501.39722   144.66748    -3.46586   .00052998  -784.96263   -217.8318       15340   1.9601186           0
2006.jahrend  -407.48888   199.56608  -2.0418744   .04118106  -798.66208  -16.315681       15340   1.9601186           0
2007.jahrend  -159.93734   266.61608   -.5998788   .54859587  -682.53649   362.66182       15340   1.9601186           0
2008.jahrend  -114.46446   308.17776  -.37142348   .71032727  -718.52944   489.60052       15340   1.9601186           0
2009.jahrend  -566.49254   121.93596  -4.6458204   3.415e-06  -805.50148   -327.4836       15340   1.9601186           0
2010.jahrend  -45.657783    516.8736  -.08833452   .92961195  -1058.7914    967.4758       15340   1.9601186           0
2011.jahrend  -457.40175   168.27422  -2.7181926   .00657134  -787.23919  -127.56431       15340   1.9601186           0
2012.jahrend  -340.88202   310.77902  -1.0968631   .27271848  -950.04577   268.28172       15340   1.9601186           0
2013.jahrend    55.44111   583.75962    .0949725   .92433791   -1088.797   1199.6792       15340   1.9601186           0
2014.jahrend   212.95989    315.8767   .67418676   .50020275  -406.19592   832.11569       15340   1.9601186           0
2015.jahrend   1981.0713   1727.1389   1.1470249   .25138924  -1404.3259   5366.4685       15340   1.9601186           0
2016.jahrend   1001.2241    625.9665   1.5994852   .10973344  -225.74449   2228.1927       15340   1.9601186           0
2017.jahrend  -205.79895   526.15287  -.39113908   .69569987   -1237.121    825.5231       15340   1.9601186           0
       _cons   1304.6566   72.083805   18.099164   1.834e-72   1163.3638   1445.9494       15340   1.9601186           0
r; t=0.05 15:55:27
.         putexcel set  results/${samplefolder}/5_sample${sample}_wnc_offer_reg_ijahrend, replace
Note: File will be replaced when the first putexcel command is issued.
r; t=0.04 15:55:27
.         putexcel A1=matrix(reg_ijahrend), names
file results/two/5_sample2_wnc_offer_reg_ijahrend.xlsx saved
r; t=0.09 15:55:27
.         `putexcelclose'
r; t=0.00 15:55:27
. }
r; t=0.19 15:55:27

. 
. capture noisily reg wnc_offer_beg_monthly wcoal_end_monthly  jahrend, robust

Linear regression                               Number of obs     =     15,383
                                                F(2, 15380)       =     155.50
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1406
                                                Root MSE          =     519.42

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .3165245   .0216297    14.63   0.000     .2741277    .3589213
          jahrend |   -10.5357    3.14199    -3.35   0.001    -16.69438   -4.377032
            _cons |   21900.55   6279.604     3.49   0.000     9591.786    34209.32
-----------------------------------------------------------------------------------
r; t=0.13 15:55:27

. if _rc!=2000 & _rc!=2001 & ${iab}==1{
.         matrix output2=r(table)
r; t=0.00 15:55:27
.         mat reg_jahrend=output2'
r; t=0.00 15:55:27
.         matrix list reg_jahrend

reg_jahrend[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .31652451   .02162971   14.633784   3.604e-48   .27412772    .3589213       15380   1.9601182           0
     jahrend  -10.535704   3.1419904  -3.3531943   .00080077  -16.694377  -4.3770317       15380   1.9601182           0
       _cons   21900.551   6279.6036    3.487569   .00048879   9591.7856   34209.317       15380   1.9601182           0
r; t=0.00 15:55:27
.         putexcel set  results/${samplefolder}/5_sample${sample}_wnc_offer_reg_jahrend, replace
Note: File will be replaced when the first putexcel command is issued.
r; t=0.06 15:55:27
.         putexcel A1=matrix(reg_jahrend), names
file results/two/5_sample2_wnc_offer_reg_jahrend.xlsx saved
r; t=0.08 15:55:28
.         `putexcelclose'
r; t=0.00 15:55:28
. }
r; t=0.15 15:55:28

. 
. capture noisily reg wnc_offer_beg_monthly wcoal_end_monthly  i.decades, robust

Linear regression                               Number of obs     =     15,383
                                                F(4, 15378)       =     234.02
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1746
                                                Root MSE          =     509.08

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .2710493   .0204393    13.26   0.000      .230986    .3111127
                  |
          decades |
           1990s  |  -460.2951     25.645   -17.95   0.000    -510.5623   -410.0278
           2000s  |  -337.4986   56.27574    -6.00   0.000    -447.8057   -227.1915
           2010s  |   261.2503   357.5913     0.73   0.465     -439.671    962.1715
                  |
            _cons |   1432.652   60.01054    23.87   0.000     1315.024     1550.28
-----------------------------------------------------------------------------------
r; t=0.20 15:55:28

. if _rc!=2000 & _rc!=2001 & ${iab}==1{
.         matrix output3=r(table)
r; t=0.00 15:55:28
.         mat reg_idecades=output3'
r; t=0.00 15:55:28
.         matrix list reg_idecades

reg_idecades[6,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .27104934   .02043926   13.261211   6.436e-40   .23098597   .31111271       15378   1.9601183           0
  1b.decades           0           .           .           .           .           .       15378   1.9601183           0
   2.decades  -460.29507      25.645  -17.948726   2.620e-71   -510.5623  -410.02784       15378   1.9601183           0
   3.decades  -337.49858   56.275742  -5.9972303   2.052e-09   -447.8057  -227.19147       15378   1.9601183           0
   4.decades   261.25027   357.59132   .73058335   .46504481  -439.67101   962.17155       15378   1.9601183           0
       _cons    1432.652   60.010542    23.87334   1.02e-123   1315.0243   1550.2798       15378   1.9601183           0
r; t=0.00 15:55:28
.         putexcel set  results/${samplefolder}/5_sample${sample}_wnc_offer_reg_idecades, replace
Note: File will be replaced when the first putexcel command is issued.
r; t=0.05 15:55:28
.         putexcel A1=matrix(reg_idecades), names
file results/two/5_sample2_wnc_offer_reg_idecades.xlsx saved
r; t=0.10 15:55:28
.         `putexcelclose'
r; t=0.00 15:55:28
. }
r; t=0.16 15:55:28

. 
. capture noisily reg wnc_offer_beg_monthly wcoal_end_monthly  decades, robust

Linear regression                               Number of obs     =     15,383
                                                F(2, 15380)       =     191.07
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1450
                                                Root MSE          =      518.1

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .3097178   .0220216    14.06   0.000     .2665529    .3528827
          decades |  -169.3468   38.29383    -4.42   0.000    -244.4072   -94.28638
            _cons |    1252.14   107.3983    11.66   0.000     1041.626    1462.653
-----------------------------------------------------------------------------------
r; t=0.13 15:55:28

. if _rc!=2000 & _rc!=2001 & ${iab}==1{
.         matrix output4=r(table)
r; t=0.00 15:55:28
.         mat reg_decades=output4'
r; t=0.00 15:55:28
.         matrix list reg_decades

reg_decades[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y    .3097178    .0220216   14.064276   1.190e-44   .26655286   .35288273       15380   1.9601182           0
     decades  -169.34681   38.293826  -4.4223005   9.833e-06  -244.40723  -94.286381       15380   1.9601182           0
       _cons   1252.1397   107.39828   11.658843   2.800e-31   1041.6264    1462.653       15380   1.9601182           0
r; t=0.00 15:55:28
.         putexcel set  results/${samplefolder}/5_sample${sample}_wnc_offer_reg_decades, replace
Note: File will be replaced when the first putexcel command is issued.
r; t=0.06 15:55:28
.         putexcel A1=matrix(reg_decades), names
file results/two/5_sample2_wnc_offer_reg_decades.xlsx saved
r; t=0.08 15:55:28
.         `putexcelclose'
r; t=0.00 15:55:28
. }
r; t=0.15 15:55:28

. 
. forvalues i = 1/$cellnumber {
  2.         capture noisily reg wnc_offer_beg_monthly wcoal_end_monthly  i.jahrend  if cell == `i', robust
  3.         if _rc!=2000 & _rc!=2001 & ${iab}==1{
  4.                 matrix output1_`i'=r(table)
  5.                 mat reg_ijahrend_`i'=output1_`i''
  6.                 matrix list reg_ijahrend_`i'
  7.                 putexcel set  results/${samplefolder}/5_sample${sample}_wnc_offer_reg_ijahrend_`i', replace
  8.                 putexcel A1=matrix(reg_ijahrend_`i'), names
  9.                 `putexcelclose'
 10.         }
 11.         capture noisily reg wnc_offer_beg_monthly wcoal_end_monthly  jahrend            if cell == `i', robust
 12.         if _rc!=2000 & _rc!=2001 & ${iab}==1{
 13.                 matrix output2_`i'=r(table)
 14.                 mat reg_jahrend_`i'=output2_`i''
 15.                 matrix list reg_jahrend_`i'
 16.                 putexcel set  results/${samplefolder}/5_sample${sample}_wnc_offer_reg_jahrend_`i', replace
 17.                 putexcel A1=matrix(reg_jahrend_`i'), names
 18.                 `putexcelclose'
 19.         }
 20.         capture noisily reg wnc_offer_beg_monthly wcoal_end_monthly  i.decades if cell == `i', robust
 21.         if _rc!=2000 & _rc!=2001 & ${iab}==1{
 22.                 matrix output3_`i'=r(table)
 23.                 mat reg_idecades_`i'=output3_`i''
 24.                 matrix list reg_idecades_`i'
 25.                 putexcel set  results/${samplefolder}/5_sample${sample}_wnc_offer_reg_idecades_`i', replace
 26.                 putexcel A1=matrix(reg_idecades_`i'), names
 27.                 `putexcelclose'
 28.         }
 29.         capture noisily reg wnc_offer_beg_monthly wcoal_end_monthly  decades    if cell == `i', robust
 30.         if _rc!=2000 & _rc!=2001 & ${iab}==1{
 31.                 matrix output4_`i'=r(table)
 32.                 mat reg_decades_`i'=output4_`i''
 33.                 matrix list reg_decades_`i'
 34.                 putexcel set  results/${samplefolder}/5_sample${sample}_wnc_offer_reg_decades_`i', replace
 35.                 putexcel A1=matrix(reg_decades_`i'), names
 36.                 `putexcelclose'
 37.                 }
 38. } 

Linear regression                               Number of obs     =        341
                                                F(21, 313)        =          .
                                                Prob > F          =          .
                                                R-squared         =     0.2033
                                                Root MSE          =     491.78

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .1464416   .0609811     2.40   0.017     .0264569    .2664263
                  |
          jahrend |
            1980  |   189.8374   266.9278     0.71   0.477    -335.3622     715.037
            1981  |   565.8033   221.0351     2.56   0.011     130.9008    1000.706
            1982  |  -26.68163   345.2016    -0.08   0.938    -705.8907    652.5274
            1983  |   856.3306   214.2175     4.00   0.000     434.8423    1277.819
            1984  |   246.3908   246.6276     1.00   0.319    -238.8668    731.6485
            1985  |  -8.571403   318.6959    -0.03   0.979    -635.6285    618.4857
            1986  |    511.995    283.387     1.81   0.072     -45.5893    1069.579
            1987  |   469.5929   292.1943     1.61   0.109    -105.3204    1044.506
            1988  |   144.3109   320.9592     0.45   0.653    -487.1994    775.8212
            1989  |   831.5783   352.2045     2.36   0.019     138.5905    1524.566
            1990  |   707.8246   551.6649     1.28   0.200    -377.6159    1793.265
            1991  |  -109.5521   498.1024    -0.22   0.826    -1089.605    870.5003
            1992  |   28.28714   220.3525     0.13   0.898    -405.2722    461.8465
            1993  |  -124.1402   219.1485    -0.57   0.571    -555.3306    307.0503
            1994  |    -76.578   227.7035    -0.34   0.737    -524.6011    371.4451
            1995  |  -190.5418   223.6267    -0.85   0.395    -630.5434    249.4598
            1996  |  -219.6812   228.8225    -0.96   0.338     -669.906    230.5435
            1998  |   360.9823   211.6398     1.71   0.089    -55.43425    777.3988
            1999  |   185.5499    275.823     0.67   0.502    -357.1517    728.2516
            2000  |   156.1011   247.3076     0.63   0.528    -330.4945    642.6966
            2001  |   61.95531   230.3997     0.27   0.788    -391.3728    515.2834
            2002  |   538.6318   264.2996     2.04   0.042     18.60321     1058.66
            2003  |  -1110.093   212.9041    -5.21   0.000    -1528.997   -691.1887
            2006  |  -938.6788   210.5166    -4.46   0.000    -1352.885   -524.4722
            2012  |   50.68158   241.5532     0.21   0.834    -424.5917    525.9549
            2014  |   951.1242   217.6998     4.37   0.000     522.7842    1379.464
                  |
            _cons |   1358.446   229.0563     5.93   0.000     907.7611    1809.131
-----------------------------------------------------------------------------------

reg_ijahrend_1[29,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .14644162   .06098109    2.401427   .01691413   .02645694    .2664263         313    1.967572           0
1979b.jahr~d           0           .           .           .           .           .         313    1.967572           0
1980.jahrend    189.8374   266.92775   .71119393   .47749372  -335.36218   715.03698         313    1.967572           0
1981.jahrend   565.80332    221.0351   2.5597894   .01094304   130.90084   1000.7058         313    1.967572           0
1982.jahrend  -26.681627   345.20161  -.07729288   .93843994  -705.89066    652.5274         313    1.967572           0
1983.jahrend   856.33062   214.21751    3.997482    .0000799   434.84225    1277.819         313    1.967572           0
1984.jahrend   246.39084   246.62762   .99903992   .31854706  -238.86677   731.64845         313    1.967572           0
1985.jahrend  -8.5714029   318.69587  -.02689524   .97856043  -635.62847   618.48567         313    1.967572           0
1986.jahrend   511.99499   283.38698   1.8066991   .07176947  -45.589299   1069.5793         313    1.967572           0
1987.jahrend   469.59293   292.19432   1.6071255   .10903512  -105.32043   1044.5063         313    1.967572           0
1988.jahrend   144.31088   320.95919   .44962376   .65329295  -487.19944   775.82119         313    1.967572           0
1989.jahrend   831.57827   352.20454   2.3610663   .01883505   138.59048   1524.5661         313    1.967572           0
1990.jahrend   707.82458   551.66494   1.2830697   .20041688  -377.61593   1793.2651         313    1.967572           0
1991.jahrend  -109.55212   498.10242  -.21993894   .82606208  -1089.6045   870.50027         313    1.967572           0
1992.jahrend   28.287145   220.35248   .12837225   .89793683  -405.27224   461.84653         313    1.967572           0
1993.jahrend  -124.14017   219.14851  -.56646594   .57148303  -555.33065   307.05031         313    1.967572           0
1994.jahrend  -76.578002   227.70352  -.33630574   .73686567  -524.60109   371.44508         313    1.967572           0
1995.jahrend  -190.54183   223.62667  -.85205325     .394836  -630.54342   249.45975         313    1.967572           0
1996.jahrend  -219.68124   228.82249  -.96005089   .33777063  -669.90597   230.54349         313    1.967572           0
1998.jahrend   360.98228   211.63979   1.7056447   .08906632  -55.434247   777.39881         313    1.967572           0
1999.jahrend   185.54995   275.82301    .6727138   .50162572   -357.1517   728.25159         313    1.967572           0
2000.jahrend   156.10107   247.30762   .63120202    .5283691  -330.49448   642.69662         313    1.967572           0
2001.jahrend   61.955309   230.39975   .26890354   .78818107  -391.37279   515.28341         313    1.967572           0
2002.jahrend    538.6318   264.29965   2.0379588   .04239452   18.603213   1058.6604         313    1.967572           0
2003.jahrend  -1110.0929    212.9041  -5.2140513   3.364e-07   -1528.997  -691.18873         313    1.967572           0
2006.jahrend  -938.67879   210.51661  -4.4589297   .00001149  -1352.8854  -524.47219         313    1.967572           0
2012.jahrend    50.68158    241.5532    .2098154   .83394826  -424.59173   525.95489         313    1.967572           0
2014.jahrend   951.12423   217.69981   4.3689713     .000017   522.78417   1379.4643         313    1.967572           0
       _cons   1358.4459   229.05634   5.9306191   7.980e-09   907.76109   1809.1308         313    1.967572           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_ijahrend_1.xlsx saved

Linear regression                               Number of obs     =        341
                                                F(2, 338)         =       3.63
                                                Prob > F          =     0.0275
                                                R-squared         =     0.0332
                                                Root MSE          =     521.31

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .0983952   .0502478     1.96   0.051    -.0004426    .1972329
          jahrend |  -11.31329   6.090891    -1.86   0.064    -23.29412     .667534
            _cons |   24012.88   12135.61     1.98   0.049      142.052     47883.7
-----------------------------------------------------------------------------------

reg_jahrend_1[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .09839519   .05024778   1.9581996   .05102883  -.00044257   .19723294         338   1.9670073           0
     jahrend  -11.313294   6.0908912  -1.8574118   .06412216  -23.294121   .66753404         338   1.9670073           0
       _cons   24012.876   12135.605   1.9787127   .04865977   142.05197   47883.701         338   1.9670073           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_jahrend_1.xlsx saved

Linear regression                               Number of obs     =        341
                                                F(4, 336)         =       6.15
                                                Prob > F          =     0.0001
                                                R-squared         =     0.0892
                                                Root MSE          =     507.49

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .1142976   .0525411     2.18   0.030     .0109466    .2176486
                  |
          decades |
           1990s  |  -338.1356   78.33536    -4.32   0.000    -492.2252   -184.0461
           2000s  |  -176.3928   135.2143    -1.30   0.193     -442.366    89.58041
           2010s  |   233.3763   296.0244     0.79   0.431    -348.9184    815.6709
                  |
            _cons |   1690.839   124.4169    13.59   0.000     1446.105    1935.573
-----------------------------------------------------------------------------------

reg_idecades_1[6,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y    .1142976   .05254113   2.1753931   .03029737   .01094662   .21764859         336   1.9670494           0
  1b.decades           0           .           .           .           .           .         336   1.9670494           0
   2.decades  -338.13563    78.33536  -4.3165133   .00002088  -492.22515  -184.04611         336   1.9670494           0
   3.decades  -176.39278   135.21429  -1.3045424   .19294155  -442.36598   89.580415         336   1.9670494           0
   4.decades   233.37627   296.02442   .78836829   .43103717  -348.91839   815.67092         336   1.9670494           0
       _cons    1690.839   124.41685   13.590112   7.957e-34   1446.1049    1935.573         336   1.9670494           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_idecades_1.xlsx saved

Linear regression                               Number of obs     =        341
                                                F(2, 338)         =       3.59
                                                Prob > F          =     0.0286
                                                R-squared         =     0.0351
                                                Root MSE          =     520.79

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .0869394   .0499875     1.74   0.083    -.0113863    .1852651
          decades |  -116.5098    63.2261    -1.84   0.066     -240.876    7.856396
            _cons |   1716.849   168.0593    10.22   0.000     1386.275    2047.423
-----------------------------------------------------------------------------------

reg_decades_1[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .08693941   .04998745   1.7392247   .08290566  -.01138627   .18526509         338   1.9670073           0
     decades   -116.5098   63.226098  -1.8427486    .0662413    -240.876   7.8563963         338   1.9670073           0
       _cons   1716.8491   168.05927   10.215736   1.575e-21   1386.2753    2047.423         338   1.9670073           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_decades_1.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(10, 51)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.6723
                                                Root MSE          =     422.98

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |  -.0323426    .094346    -0.34   0.733      -.22175    .1570649
                  |
          jahrend |
            1979  |   82.50516   176.0496     0.47   0.641    -270.9291    435.9394
            1980  |    32.8712   143.7683     0.23   0.820    -255.7556     321.498
            1981  |   421.9602   180.4542     2.34   0.023     59.68324    784.2372
            1982  |   266.2082   262.3554     1.01   0.315    -260.4923    792.9088
            1983  |  -426.7303   334.8273    -1.27   0.208    -1098.924    245.4635
            1984  |   612.8892   229.8085     2.67   0.010     151.5294    1074.249
            1991  |   1312.779   424.3263     3.09   0.003     460.9087     2164.65
            2002  |   101.5405   193.9678     0.52   0.603    -287.8662    490.9471
            2008  |   -1458.93   215.6706    -6.76   0.000    -1891.907   -1025.953
            2009  |  -164.8891   299.9133    -0.55   0.585    -766.9903     437.212
            2010  |   3296.723   222.3234    14.83   0.000      2850.39    3743.056
            2011  |   89.37122   293.6505     0.30   0.762    -500.1569    678.8993
            2013  |  -569.1711   226.2233    -2.52   0.015    -1023.333   -115.0089
            2017  |    773.333   358.4054     2.16   0.036     53.80414    1492.862
                  |
            _cons |   1895.607   167.1815    11.34   0.000     1559.976    2231.238
-----------------------------------------------------------------------------------

reg_ijahrend_2[17,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y  -.03234255   .09434598  -.34280797    .7331519  -.22175001    .1570649          NA   2.0075838           0
1978b.jahr~d           0           .           .           .           .           .          NA   2.0075838           0
1979.jahrend   82.505159   176.04957   .46864732   .64131879   -270.9291   435.93942          NA   2.0075838           0
1980.jahrend   32.871201   143.76827   .22864017   .82006294  -255.75564   321.49805          NA   2.0075838           0
1981.jahrend   421.96023   180.45423   2.3383227   .02332938   59.683236   784.23722          NA   2.0075838           0
1982.jahrend   266.20823   262.35545   1.0146854   .31504562  -260.49231   792.90877          NA   2.0075838           0
1983.jahrend  -426.73034   334.82731  -1.2744789    .2082703  -1098.9242   245.46352          NA   2.0075838           0
1984.jahrend   612.88921   229.80849   2.6669564   .01023113   151.52942    1074.249          NA   2.0075838           0
1991.jahrend   1312.7793   424.32629   3.0937967   .00320371   460.90868   2164.6498          NA   2.0075838           0
2002.jahrend   101.54046   193.96781    .5234913    .6028998  -287.86616   490.94708          NA   2.0075838           0
2008.jahrend  -1458.9298   215.67065  -6.7646191   1.281e-08  -1891.9067  -1025.9529          NA   2.0075838           0
2009.jahrend  -164.88911   299.91334  -.54978921   .58486372  -766.99026   437.21203          NA   2.0075838           0
2010.jahrend   3296.7234   222.32345   14.828501   3.949e-20   2850.3905   3743.0564          NA   2.0075838           0
2011.jahrend   89.371215   293.65055   .30434548   .76210312  -500.15686   678.89929          NA   2.0075838           0
2013.jahrend  -569.17105   226.22328  -2.5159703   .01506053  -1023.3332  -115.00886          NA   2.0075838           0
2017.jahrend   773.33304   358.40542   2.1577046    .0356839   53.804137   1492.8619          NA   2.0075838           0
       _cons    1895.607   167.18155   11.338613   1.499e-15    1559.976   2231.2379          NA   2.0075838           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_ijahrend_2.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(2, 64)          =       0.01
                                                Prob > F          =     0.9858
                                                R-squared         =     0.0008
                                                Root MSE          =     659.37

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .0094294   .1125404     0.08   0.933    -.2153958    .2342547
          jahrend |   .7915737   15.22174     0.05   0.959    -29.61735     31.2005
            _cons |   466.7825   30080.92     0.02   0.988    -59626.76    60560.32
-----------------------------------------------------------------------------------

reg_jahrend_2[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .00942944   .11254038   .08378717   .93348718  -.21539582    .2342547          NA   1.9977297           0
     jahrend   .79157365   15.221744   .05200282   .95868842  -29.617355   31.200502          NA   1.9977297           0
       _cons   466.78253   30080.918   .01551756   .98766755   -59626.76   60560.325          NA   1.9977297           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_jahrend_2.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(3, 63)          =       1.64
                                                Prob > F          =     0.1900
                                                R-squared         =     0.0894
                                                Root MSE          =     634.43

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .0570002   .1247485     0.46   0.649    -.1922898    .3062902
                  |
          decades |
           2000s  |  -793.8019   418.0249    -1.90   0.062    -1629.158    41.55412
           2010s  |   140.8987   617.1031     0.23   0.820    -1092.284    1374.081
                  |
            _cons |   2003.183   126.6979    15.81   0.000     1749.998    2256.369
-----------------------------------------------------------------------------------

reg_idecades_2[5,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .05700023    .1247485   .45692119   .64929932  -.19228976   .30629023          NA   1.9983405           0
  1b.decades           0           .           .           .           .           .          NA   1.9983405           0
   3.decades  -793.80195   418.02488  -1.8989347   .06215446   -1629.158   41.554121          NA   1.9983405           0
   4.decades   140.89869   617.10314   .22832275   .82013482  -1092.2835   1374.0809          NA   1.9983405           0
       _cons   2003.1831   126.69789   15.810707   1.313e-23   1749.9976   2256.3686          NA   1.9983405           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_idecades_2.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(2, 64)          =       0.02
                                                Prob > F          =     0.9756
                                                R-squared         =     0.0008
                                                Root MSE          =     659.36

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |    .024106   .1296086     0.19   0.853    -.2348169    .2830289
          decades |  -10.38406   196.2262    -0.05   0.958     -402.391    381.6229
            _cons |   2032.472   156.7277    12.97   0.000     1719.373    2345.572
-----------------------------------------------------------------------------------

reg_decades_2[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y     .024106   .12960859   .18599079   .85304024  -.23481691   .28302892          NA   1.9977297           0
     decades  -10.384057   196.22621   -.0529188   .95796144  -402.39098   381.62286          NA   1.9977297           0
       _cons   2032.4721    156.7277   12.968174   1.434e-19   1719.3725   2345.5717          NA   1.9977297           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_decades_2.xlsx saved

Linear regression                               Number of obs     =        337
                                                F(16, 313)        =          .
                                                Prob > F          =          .
                                                R-squared         =     0.2897
                                                Root MSE          =     471.21

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .2377488   .0750369     3.17   0.002     .0901082    .3853893
                  |
          jahrend |
            1979  |   129.1586   336.7615     0.38   0.702    -533.4438     791.761
            1980  |   383.3141   380.8011     1.01   0.315    -365.9395    1132.568
            1981  |   986.8198   334.2468     2.95   0.003     329.1651    1644.475
            1982  |  -135.3059   580.7351    -0.23   0.816    -1277.944    1007.332
            1983  |  -461.1224   505.6878    -0.91   0.363      -1456.1    533.8548
            1984  |    55.6266   367.1135     0.15   0.880    -666.6957    777.9489
            1985  |    980.052   433.2023     2.26   0.024     127.6954    1832.409
            1986  |   384.7263   328.0459     1.17   0.242    -260.7276     1030.18
            1990  |  -616.6169   487.8495    -1.26   0.207    -1576.496    343.2621
            1991  |  -1480.897   335.8429    -4.41   0.000    -2141.693   -820.1022
            1992  |  -397.9956   332.3476    -1.20   0.232    -1051.913    255.9223
            1993  |   -490.752    327.751    -1.50   0.135    -1135.626    154.1217
            1994  |  -584.5113    332.538    -1.76   0.080    -1238.804     69.7812
            1995  |  -269.3823   344.9151    -0.78   0.435    -948.0277     409.263
            1996  |   -328.636   339.2714    -0.97   0.333    -996.1768    338.9049
            1997  |    5.00526   444.6032     0.01   0.991    -869.7835    879.7941
            1998  |   150.8807   564.3005     0.27   0.789    -959.4212    1261.183
            1999  |  -992.0013   527.2036    -1.88   0.061    -2029.312    45.30973
            2001  |  -1730.605   338.9402    -5.11   0.000    -2397.494   -1063.716
            2002  |   886.1557   326.0436     2.72   0.007     244.6415     1527.67
            2003  |   1103.426   326.0836     3.38   0.001      461.833    1745.019
            2005  |   164.3318   328.5653     0.50   0.617    -482.1441    810.8077
                  |
            _cons |   1468.872   384.1724     3.82   0.000     712.9854    2224.759
-----------------------------------------------------------------------------------

reg_ijahrend_3[25,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .23774875   .07503694   3.1684227   .00168403   .09010816   .38538934         313    1.967572           0
1977b.jahr~d           0           .           .           .           .           .         313    1.967572           0
1979.jahrend   129.15861   336.76146   .38353146   .70158613  -533.44381   791.76104         313    1.967572           0
1980.jahrend   383.31414   380.80111   1.0065993   .31490488  -365.93946   1132.5677         313    1.967572           0
1981.jahrend   986.81979   334.24683   2.9523684   .00339199   329.16507   1644.4745         313    1.967572           0
1982.jahrend  -135.30593   580.73512  -.23299078   .81592085  -1277.9441   1007.3322         313    1.967572           0
1983.jahrend   -461.1224    505.6878  -.91187172   .36253802  -1456.0996   533.85476         313    1.967572           0
1984.jahrend   55.626601   367.11351   .15152425   .87965979  -666.69567   777.94887         313    1.967572           0
1985.jahrend   980.05205   433.20228   2.2623428   .02436155   127.69537   1832.4087         313    1.967572           0
1986.jahrend   384.72631   328.04589    1.172782     .241775   -260.7276   1030.1802         313    1.967572           0
1990.jahrend  -616.61685   487.84946   -1.263949   .20718881  -1576.4958    343.2621         313    1.967572           0
1991.jahrend  -1480.8974   335.84293   -4.409494   .00001426  -2141.6925  -820.10221         313    1.967572           0
1992.jahrend   -397.9956   332.34763   -1.197528   .23200691  -1051.9135   255.92229         313    1.967572           0
1993.jahrend  -490.75198   327.75098  -1.4973319   .13531466  -1135.6256   154.12168         313    1.967572           0
1994.jahrend  -584.51126     332.538  -1.7577277   .07977134  -1238.8037     69.7812         313    1.967572           0
1995.jahrend  -269.38234   344.91513  -.78101052    .4353866   -948.0277   409.26301         313    1.967572           0
1996.jahrend  -328.63596   339.27138  -.96865218   .33346665  -996.17682   338.90491         313    1.967572           0
1997.jahrend   5.0052599   444.60319   .01125781   .99102493  -869.78354   879.79406         313    1.967572           0
1998.jahrend    150.8807    564.3005   .26737651   .78935538  -959.42118   1261.1826         313    1.967572           0
1999.jahrend   -992.0013   527.20359  -1.8816285   .06081431  -2029.3123   45.309734         313    1.967572           0
2001.jahrend   -1730.605   338.94021    -5.10593   5.728e-07  -2397.4943  -1063.7157         313    1.967572           0
2002.jahrend   886.15569   326.04358   2.7179057   .00693565   244.64147   1527.6699         313    1.967572           0
2003.jahrend    1103.426    326.0836   3.3838746   .00080562   461.83304    1745.019         313    1.967572           0
2005.jahrend   164.33179   328.56531   .50014954   .61732124  -482.14413    810.8077         313    1.967572           0
       _cons   1468.8722   384.17236   3.8234718   .00015878    712.9854    2224.759         313    1.967572           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_ijahrend_3.xlsx saved

Linear regression                               Number of obs     =        337
                                                F(2, 334)         =       8.50
                                                Prob > F          =     0.0003
                                                R-squared         =     0.0738
                                                Root MSE          =      520.9

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .2551351    .074971     3.40   0.001     .1076603      .40261
          jahrend |  -19.48828   12.02505    -1.62   0.106    -43.14265    4.166096
            _cons |   39889.94   23995.66     1.66   0.097    -7311.726    87091.62
-----------------------------------------------------------------------------------

reg_jahrend_3[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .25513515   .07497098    3.403119   .00074721   .10766035   .40260995         334    1.967092           0
     jahrend  -19.488279   12.025048  -1.6206405   .10603844  -43.142654   4.1660956         334    1.967092           0
       _cons   39889.945   23995.661   1.6623816   .09737442  -7311.7263   87091.616         334    1.967092           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_jahrend_3.xlsx saved

Linear regression                               Number of obs     =        337
                                                F(3, 333)         =       5.79
                                                Prob > F          =     0.0007
                                                R-squared         =     0.1091
                                                Root MSE          =     511.64

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |    .187494   .0781883     2.40   0.017     .0336888    .3412992
                  |
          decades |
           1990s  |  -449.5111   161.8518    -2.78   0.006    -767.8918   -131.1303
           2000s  |    129.532   566.2081     0.23   0.819    -984.2636    1243.328
                  |
            _cons |   1611.725   244.6798     6.59   0.000     1130.412    2093.038
-----------------------------------------------------------------------------------

reg_idecades_3[5,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .18749401   .07818829   2.3979809   .01703576   .03368878   .34129924         333   1.9671134           0
  1b.decades           0           .           .           .           .           .         333   1.9671134           0
   2.decades  -449.51107   161.85176  -2.7773011   .00579167  -767.89184   -131.1303         333   1.9671134           0
   3.decades   129.53198    566.2081   .22877098   .81918735  -984.26358   1243.3275         333   1.9671134           0
       _cons   1611.7248   244.67982   6.5870769   1.751e-10   1130.4118   2093.0377         333   1.9671134           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_idecades_3.xlsx saved

Linear regression                               Number of obs     =        337
                                                F(2, 334)         =       7.34
                                                Prob > F          =     0.0008
                                                R-squared         =     0.0771
                                                Root MSE          =     519.98

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .2413037   .0767437     3.14   0.002     .0903417    .3922657
          decades |  -264.6514   180.8311    -1.46   0.144    -620.3629     91.0601
            _cons |   1595.002   418.5202     3.81   0.000     771.7341    2418.269
-----------------------------------------------------------------------------------

reg_decades_3[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .24130369   .07674373   3.1442789   .00181437   .09034172   .39226566         334    1.967092           0
     decades  -264.65138   180.83114  -1.4635277   .14426349  -620.36286     91.0601         334    1.967092           0
       _cons   1595.0018   418.52017   3.8110512   .00016472   771.73413   2418.2695         334    1.967092           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_decades_3.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(6, 84)          =          .
                                                Prob > F          =          .
                                                R-squared         =     0.3518
                                                Root MSE          =     400.53

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .0815465   .0572358     1.42   0.158    -.0322732    .1953662
                  |
          jahrend |
            1981  |  -25.85093    237.718    -0.11   0.914    -498.5793    446.8774
            1982  |   94.02722   92.78655     1.01   0.314      -90.489    278.5434
            1983  |   239.5655    108.825     2.20   0.030     23.15505    455.9759
            1987  |   456.7723   75.81391     6.02   0.000     306.0081    607.5366
            1988  |   14.10144   233.9911     0.06   0.952    -451.2156    479.4185
            1991  |   430.7546   278.0105     1.55   0.125    -122.0999     983.609
            2008  |   2315.091   114.5765    20.21   0.000     2087.243    2542.939
            2015  |   557.8158   119.2823     4.68   0.000     320.6099    795.0218
            2017  |   714.8219   93.42439     7.65   0.000     529.0372    900.6065
                  |
            _cons |   1606.356   169.1428     9.50   0.000     1269.997    1942.715
-----------------------------------------------------------------------------------

reg_ijahrend_4[12,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y    .0815465   .05723584   1.4247454   .15793565  -.03227324   .19536624          NA   1.9886097           0
1980b.jahr~d           0           .           .           .           .           .          NA   1.9886097           0
1981.jahrend  -25.850929   237.71802  -.10874619   .91366316  -498.57927   446.87741          NA   1.9886097           0
1982.jahrend   94.027219   92.786546   1.0133713   .31379294  -90.489004   278.54344          NA   1.9886097           0
1983.jahrend   239.56549     108.825   2.2013829   .03045147    23.15505   455.97593          NA   1.9886097           0
1987.jahrend   456.77234    75.81391   6.0249146   4.328e-08   306.00806   607.53661          NA   1.9886097           0
1988.jahrend   14.101445   233.99115   .06026486   .95208784  -451.21561    479.4185          NA   1.9886097           0
1991.jahrend   430.75457   278.01054   1.5494181   .12504083  -122.09988   983.60902          NA   1.9886097           0
2008.jahrend   2315.0909   114.57649   20.205636   5.313e-34    2087.243   2542.9388          NA   1.9886097           0
2015.jahrend   557.81584   119.28231    4.676434   .00001104   320.60989   795.02179          NA   1.9886097           0
2017.jahrend   714.82187   93.424393   7.6513408   3.011e-11   529.03722   900.60652          NA   1.9886097           0
       _cons   1606.3562   169.14276   9.4970438   5.932e-15   1269.9973   1942.7151          NA   1.9886097           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_ijahrend_4.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(2, 92)          =       4.43
                                                Prob > F          =     0.0146
                                                R-squared         =     0.2123
                                                Root MSE          =     421.92

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |    .076882   .0389983     1.97   0.052     -.000572     .154336
          jahrend |   32.07158    13.0106     2.47   0.016     6.231403    57.91176
            _cons |  -61833.63   25808.39    -2.40   0.019    -113091.3   -10575.95
-----------------------------------------------------------------------------------

reg_jahrend_4[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .07688201   .03899832   1.9714185   .05168054  -.00057202   .15433603          NA   1.9860863           0
     jahrend   32.071582   13.010602   2.4650344   .01555558    6.231403   57.911761          NA   1.9860863           0
       _cons   -61833.63   25808.385  -2.3958736   .01860545  -113091.31  -10575.949          NA   1.9860863           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_jahrend_4.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(2, 91)          =          .
                                                Prob > F          =          .
                                                R-squared         =     0.2849
                                                Root MSE          =     404.21

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .0866844   .0399651     2.17   0.033     .0072986    .1660702
                  |
          decades |
           2000s  |   2164.762   60.16497    35.98   0.000     2045.252    2284.272
           2010s  |   487.1145   79.39411     6.14   0.000     329.4079    644.8212
                  |
            _cons |   1735.379   129.9988    13.35   0.000     1477.152    1993.605
-----------------------------------------------------------------------------------

reg_idecades_4[5,9]
                      b         se          t     pvalue         ll         ul         df       crit      eform
wcoal_end_~y  .08668438  .03996513  2.1690002  .03268964  .00729855  .16607021         NA  1.9863772          0
  1b.decades          0          .          .          .          .          .         NA  1.9863772          0
   3.decades  2164.7621  60.164971   35.98044  1.342e-55  2045.2518  2284.2725         NA  1.9863772          0
   4.decades  487.11454  79.394113  6.1353988  2.171e-08  329.40789   644.8212         NA  1.9863772          0
       _cons  1735.3787  129.99881  13.349188  3.770e-23   1477.152  1993.6053         NA  1.9863772          0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_idecades_4.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(2, 92)          =       4.10
                                                Prob > F          =     0.0198
                                                R-squared         =     0.1522
                                                Root MSE          =     437.72

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .0989539   .0428009     2.31   0.023     .0139477    .1839601
          decades |   324.3678   168.3578     1.93   0.057    -10.00525    658.7409
            _cons |   1376.659   229.7571     5.99   0.000     920.3418    1832.977
-----------------------------------------------------------------------------------

reg_decades_4[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .09895391   .04280085    2.311961   .02301166   .01394772    .1839601          NA   1.9860863           0
     decades   324.36783   168.35778   1.9266578   .05710874  -10.005249   658.74092          NA   1.9860863           0
       _cons   1376.6592   229.75707   5.9918035   3.995e-08   920.34184   1832.9766          NA   1.9860863           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_decades_4.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(7, 55)          =          .
                                                Prob > F          =          .
                                                R-squared         =     0.8102
                                                Root MSE          =     319.96

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |  -.3556958   .1259738    -2.82   0.007    -.6081529   -.1032386
                  |
          jahrend |
            1986  |   1144.858   219.3896     5.22   0.000     705.1919    1584.525
            1992  |  -492.2204   119.2725    -4.13   0.000    -731.2479   -253.1929
            1993  |  -286.4367   69.61777    -4.11   0.000    -425.9539   -146.9196
            1994  |  -165.5598   113.7174    -1.46   0.151    -393.4546    62.33487
            1995  |  -187.7149   215.8514    -0.87   0.388    -620.2908     244.861
            1996  |  -481.8448   204.6202    -2.35   0.022    -891.9129   -71.77672
            1997  |    612.493   180.8685     3.39   0.001     250.0245    974.9616
            1998  |  -678.7197   113.1822    -6.00   0.000     -905.542   -451.8975
            2001  |   4464.892   77.15253    57.87   0.000     4310.275    4619.509
            2003  |   1251.527   210.5856     5.94   0.000     829.5041     1673.55
            2004  |   617.9892   203.4235     3.04   0.004     210.3195    1025.659
            2007  |  -250.2634   136.6016    -1.83   0.072     -524.019    23.49232
                  |
            _cons |   2543.012   331.6298     7.67   0.000     1878.411    3207.613
-----------------------------------------------------------------------------------

reg_ijahrend_5[15,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y  -.35569576    .1259738  -2.8235693   .00660082   -.6081529  -.10323862          NA   2.0040448           0
1978b.jahr~d           0           .           .           .           .           .          NA   2.0040448           0
1986.jahrend   1144.8584   219.38956   5.2183813   2.831e-06   705.19186   1584.5249          NA   2.0040448           0
1992.jahrend  -492.22041   119.27252  -4.1268551   .00012567  -731.24788  -253.19294          NA   2.0040448           0
1993.jahrend  -286.43674   69.617768  -4.1144201   .00013095  -425.95387  -146.91962          NA   2.0040448           0
1994.jahrend  -165.55984   113.71738  -1.4558887   .15110822  -393.45456   62.334871          NA   2.0040448           0
1995.jahrend  -187.71489   215.85141  -.86964865     .388272  -620.29078   244.86101          NA   2.0040448           0
1996.jahrend  -481.84483   204.62023   -2.354825   .02212775  -891.91293  -71.776723          NA   2.0040448           0
1997.jahrend   612.49305   180.86849   3.3864001   .00131361    250.0245   974.96159          NA   2.0040448           0
1998.jahrend  -678.71973   113.18223  -5.9966985   1.629e-07  -905.54199  -451.89746          NA   2.0040448           0
2001.jahrend   4464.8916   77.152526   57.870972   5.798e-51   4310.2745   4619.5088          NA   2.0040448           0
2003.jahrend    1251.527   210.58558   5.9430804   1.988e-07   829.50409     1673.55          NA   2.0040448           0
2004.jahrend   617.98924   203.42346   3.0379448   .00363925   210.31952    1025.659          NA   2.0040448           0
2007.jahrend  -250.26336   136.60158  -1.8320678   .07235795  -524.01904   23.492325          NA   2.0040448           0
       _cons   2543.0119   331.62982   7.6682244   3.035e-10   1878.4109   3207.6129          NA   2.0040448           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_ijahrend_5.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(2, 66)          =       1.81
                                                Prob > F          =     0.1719
                                                R-squared         =     0.1455
                                                Root MSE          =      619.7

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .1641516   .1633272     1.01   0.319    -.1619417    .4902449
          jahrend |    51.8469    28.5829     1.81   0.074     -5.22069    108.9145
            _cons |  -102213.7   57047.46    -1.79   0.078    -216112.6    11685.27
-----------------------------------------------------------------------------------

reg_jahrend_5[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .16415157   .16332721   1.0050473   .31854473  -.16194172   .49024486          NA   1.9965644           0
     jahrend   51.846902   28.582895   1.8139136   .07423693  -5.2206896   108.91449          NA   1.9965644           0
       _cons  -102213.66   57047.458  -1.7917303   .07776035  -216112.58   11685.267          NA   1.9965644           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_jahrend_5.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(3, 65)          =       3.23
                                                Prob > F          =     0.0278
                                                R-squared         =     0.3491
                                                Root MSE          =     544.99

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |  -.0299683   .1437061    -0.21   0.835    -.3169692    .2570326
                  |
          decades |
           1990s  |  -502.1621   298.0694    -1.68   0.097    -1097.448    93.12351
           2000s  |   885.2501   631.1707     1.40   0.166    -375.2848    2145.785
                  |
            _cons |   1974.319   545.6421     3.62   0.001      884.596    3064.041
-----------------------------------------------------------------------------------

reg_idecades_5[5,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y  -.02996829   .14370609  -.20853878   .83546042  -.31696918   .25703259          NA   1.9971379           0
  1b.decades           0           .           .           .           .           .          NA   1.9971379           0
   2.decades  -502.16209   298.06935  -1.6847156   .09683962  -1097.4477   93.123514          NA   1.9971379           0
   3.decades   885.25009    631.1707   1.4025526   .16550857  -375.28484    2145.785          NA   1.9971379           0
       _cons   1974.3185   545.64209   3.6183398   .00058039     884.596    3064.041          NA   1.9971379           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_idecades_5.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(2, 66)          =       1.89
                                                Prob > F          =     0.1586
                                                R-squared         =     0.2228
                                                Root MSE          =     590.98

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .1683156   .1665193     1.01   0.316    -.1641509    .5007821
          decades |   813.1804   467.4136     1.74   0.087     -120.041    1746.402
            _cons |  -524.5829   1019.542    -0.51   0.609    -2560.165    1510.999
-----------------------------------------------------------------------------------

reg_decades_5[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .16831558    .1665193   1.0107872   .31580969  -.16415094   .50078209          NA   1.9965644           0
     decades   813.18044   467.41363   1.7397448   .08656539  -120.04099   1746.4019          NA   1.9965644           0
       _cons  -524.58294   1019.5422  -.51452793   .60860169  -2560.1646   1510.9987          NA   1.9965644           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_decades_5.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(2, 37)          =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0550
                                                Root MSE          =        325

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .0432711   .0651769     0.66   0.511    -.0887898     .175332
                  |
          jahrend |
            1983  |    160.702   203.3397     0.79   0.434    -251.3033    572.7073
            2015  |    255.278    91.9172     2.78   0.009     69.03602    441.5199
                  |
            _cons |   1783.214    219.588     8.12   0.000     1338.286    2228.141
-----------------------------------------------------------------------------------

reg_ijahrend_6[5,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y    .0432711   .06517689   .66390254    .5108688  -.08878982   .17533201          NA   2.0261925           0
1982b.jahr~d           0           .           .           .           .           .          NA   2.0261925           0
1983.jahrend   160.70199   203.33966   .79031308    .4343813  -251.30329   572.70728          NA   2.0261925           0
2015.jahrend   255.27796   91.917197     2.77726    .0085551   69.036025   441.51989          NA   2.0261925           0
       _cons   1783.2137   219.58802   8.1207243   9.639e-10   1338.2862   2228.1413          NA   2.0261925           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_ijahrend_6.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(2, 38)          =      16.13
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0261
                                                Root MSE          =     325.57

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .0389186   .0664246     0.59   0.561     -.095551    .1733882
          jahrend |   7.960882   2.960616     2.69   0.011     1.967428    13.95434
            _cons |  -13959.47    5717.35    -2.44   0.019    -25533.64     -2385.3
-----------------------------------------------------------------------------------

reg_jahrend_6[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .03891859   .06642462   .58590615   .56140267  -.09555101    .1733882          NA   2.0243942           0
     jahrend   7.9608817   2.9606159   2.6889276    .0105852   1.9674282   13.954335          NA   2.0243942           0
       _cons   -13959.47   5717.3501  -2.4415979   .01938898   -25533.64  -2385.2996          NA   2.0243942           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_jahrend_6.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(1, 38)          =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0232
                                                Root MSE          =     326.05

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .0437248   .0710273     0.62   0.542    -.1000625    .1875121
                  |
          decades |
           2010s  |   230.5438   109.7773     2.10   0.042     8.311129    452.7764
            _cons |   1805.915   227.6677     7.93   0.000     1345.026    2266.805
-----------------------------------------------------------------------------------

reg_idecades_6[4,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .04372478   .07102732   .61560506   .54182501  -.10006252   .18751208          NA   2.0243942           0
  1b.decades           0           .           .           .           .           .          NA   2.0243942           0
   4.decades   230.54375   109.77735   2.1001031   .04241224   8.3111295   452.77637          NA   2.0243942           0
       _cons   1805.9154   227.66774   7.9322411   1.399e-09   1345.0262   2266.8047          NA   2.0243942           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_idecades_6.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(1, 38)          =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0232
                                                Root MSE          =     326.05

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .0437248   .0710273     0.62   0.542    -.1000625    .1875121
          decades |   76.84792   36.59245     2.10   0.042     2.770376    150.9255
            _cons |   1729.068   201.7783     8.57   0.000     1320.589    2137.546
-----------------------------------------------------------------------------------

reg_decades_6[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .04372478   .07102732   .61560506   .54182501  -.10006252   .18751208          NA   2.0243942           0
     decades   76.847917   36.592449   2.1001031   .04241224   2.7703765   150.92546          NA   2.0243942           0
       _cons   1729.0675   201.77832   8.5691443   2.076e-10   1320.5887   2137.5464          NA   2.0243942           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_decades_6.xlsx saved

Linear regression                               Number of obs     =      3,284
                                                F(29, 3251)       =          .
                                                Prob > F          =          .
                                                R-squared         =     0.1413
                                                Root MSE          =     425.99

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .2096551   .0293406     7.15   0.000     .1521272    .2671831
                  |
          jahrend |
            1979  |  -242.7212   312.8225    -0.78   0.438    -856.0704     370.628
            1980  |  -861.7053   228.0977    -3.78   0.000    -1308.935   -414.4755
            1981  |  -564.5518   311.2563    -1.81   0.070     -1174.83    45.72649
            1982  |  -668.7914   284.3605    -2.35   0.019    -1226.335   -111.2476
            1983  |  -163.7534   768.6872    -0.21   0.831    -1670.914    1343.407
            1984  |  -369.0918   245.1017    -1.51   0.132    -849.6613    111.4777
            1985  |  -527.4792   230.1912    -2.29   0.022    -978.8137   -76.14474
            1986  |  -20.42353   245.4854    -0.08   0.934    -501.7453    460.8983
            1987  |  -341.9597   298.5952    -1.15   0.252    -927.4135     243.494
            1988  |   -478.237    205.017    -2.33   0.020    -880.2126   -76.26141
            1989  |  -153.5239   229.3463    -0.67   0.503    -603.2019    296.1541
            1990  |   82.93303   239.4161     0.35   0.729    -386.4887    552.3548
            1991  |  -347.0349   429.4205    -0.81   0.419    -1188.997    494.9273
            1992  |  -1044.097    175.812    -5.94   0.000    -1388.811   -699.3839
            1993  |  -1088.147   175.0986    -6.21   0.000    -1431.461    -744.832
            1994  |  -1130.768   176.7138    -6.40   0.000    -1477.249   -784.2861
            1995  |  -1042.666   176.4536    -5.91   0.000    -1388.638   -696.6944
            1996  |  -976.1638   177.8278    -5.49   0.000     -1324.83    -627.498
            1997  |  -695.4615   234.0488    -2.97   0.003     -1154.36   -236.5635
            1998  |  -802.9112   264.3561    -3.04   0.002    -1321.233   -284.5898
            1999  |  -700.3633   176.6246    -3.97   0.000     -1046.67   -354.0566
            2000  |  -849.9192   236.5924    -3.59   0.000    -1313.804    -386.034
            2001  |  -896.8501    219.095    -4.09   0.000    -1326.428   -467.2718
            2002  |   -1128.05   182.8029    -6.17   0.000     -1486.47   -769.6291
            2003  |  -624.5631   188.2276    -3.32   0.001    -993.6199   -255.5063
            2004  |  -2252.361   174.5956   -12.90   0.000    -2594.689   -1910.032
            2005  |  -1226.243   394.8841    -3.11   0.002     -2000.49    -451.996
            2006  |  -1468.709   257.4051    -5.71   0.000    -1973.402   -964.0167
            2008  |  -1015.286    183.877    -5.52   0.000    -1375.813   -654.7597
            2009  |  -1830.369   175.9739   -10.40   0.000      -2175.4   -1485.338
            2012  |   -1842.61   528.5764    -3.49   0.000    -2878.987   -806.2335
                  |
            _cons |   2264.298   189.1051    11.97   0.000      1893.52    2635.075
-----------------------------------------------------------------------------------

reg_ijahrend_7[34,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .20965511   .02934061   7.1455616   1.101e-12   .15212716   .26718306        3251    1.960694           0
1977b.jahr~d           0           .           .           .           .           .        3251    1.960694           0
1979.jahrend  -242.72121   312.82251  -.77590712   .43786027  -856.07042   370.62799        3251    1.960694           0
1980.jahrend  -861.70529   228.09768  -3.7777906   .00016106   -1308.935  -414.47554        3251    1.960694           0
1981.jahrend  -564.55182   311.25628  -1.8137845   .06980306  -1174.8301   45.726486        3251    1.960694           0
1982.jahrend  -668.79137   284.36045  -2.3519142   .01873643  -1226.3352  -111.24755        3251    1.960694           0
1983.jahrend  -163.75344   768.68716  -.21303001   .83131691  -1670.9137   1343.4068        3251    1.960694           0
1984.jahrend  -369.09181   245.10173  -1.5058719   .13219721   -849.6613   111.47768        3251    1.960694           0
1985.jahrend  -527.47922   230.19119  -2.2914831   .02199915  -978.81371   -76.14474        3251    1.960694           0
1986.jahrend  -20.423532   245.48543  -.08319651   .93370041  -501.74534   460.89828        3251    1.960694           0
1987.jahrend  -341.95974   298.59519  -1.1452286   .25219879  -927.41352   243.49404        3251    1.960694           0
1988.jahrend  -478.23699   205.01699    -2.33267   .01972619  -880.21256  -76.261414        3251    1.960694           0
1989.jahrend  -153.52386   229.34635  -.66939746   .50328951  -603.20186   296.15413        3251    1.960694           0
1990.jahrend   82.933028   239.41612   .34639701   .72906679   -386.4887   552.35476        3251    1.960694           0
1991.jahrend  -347.03487   429.42049  -.80814697   .41906511   -1188.997   494.92729        3251    1.960694           0
1992.jahrend  -1044.0975   175.81203  -5.9387147   3.176e-09   -1388.811  -699.38389        3251    1.960694           0
1993.jahrend  -1088.1467   175.09859  -6.2144802   5.804e-10  -1431.4615  -744.83197        3251    1.960694           0
1994.jahrend  -1130.7678   176.71378  -6.3988658   1.790e-10  -1477.2494  -784.28614        3251    1.960694           0
1995.jahrend  -1042.6659   176.45363  -5.9090083   3.797e-09  -1388.6375  -696.69439        3251    1.960694           0
1996.jahrend  -976.16382   177.82778  -5.4893776   4.343e-08  -1324.8297  -627.49797        3251    1.960694           0
1997.jahrend  -695.46152   234.04879  -2.9714382   .00298581  -1154.3596  -236.56347        3251    1.960694           0
1998.jahrend  -802.91121   264.35611  -3.0372334   .00240656  -1321.2326  -284.58979        3251    1.960694           0
1999.jahrend  -700.36334   176.62455   -3.965266   .00007489    -1046.67  -354.05664        3251    1.960694           0
2000.jahrend  -849.91924   236.59238  -3.5923357   .00033258  -1313.8045    -386.034        3251    1.960694           0
2001.jahrend  -896.85008   219.09501    -4.09343   .00004354  -1326.4283  -467.27182        3251    1.960694           0
2002.jahrend  -1128.0497   182.80293  -6.1708512   7.631e-10  -1486.4703  -769.62907        3251    1.960694           0
2003.jahrend  -624.56311   188.22762  -3.3181268   .00091621  -993.61987  -255.50635        3251    1.960694           0
2004.jahrend  -2252.3608   174.59562  -12.900442   3.599e-37  -2594.6894  -1910.0322        3251    1.960694           0
2005.jahrend  -1226.2428   394.88407  -3.1053234   .00191708  -2000.4896  -451.99595        3251    1.960694           0
2006.jahrend  -1468.7093   257.40513  -5.7058278   1.262e-08   -1973.402  -964.01665        3251    1.960694           0
2008.jahrend  -1015.2861   183.87695  -5.5215517   3.624e-08  -1375.8125  -654.75967        3251    1.960694           0
2009.jahrend   -1830.369   175.97386  -10.401369   5.993e-25  -2175.3999  -1485.3381        3251    1.960694           0
2012.jahrend  -1842.6101   528.57642  -3.4859862   .00049682  -2878.9867  -806.23349        3251    1.960694           0
       _cons   2264.2977   189.10511   11.973752   2.314e-32   1893.5204   2635.0749        3251    1.960694           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_ijahrend_7.xlsx saved

Linear regression                               Number of obs     =      3,284
                                                F(2, 3281)        =      62.46
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0720
                                                Root MSE          =     440.83

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .2335001   .0245682     9.50   0.000     .1853297    .2816706
          jahrend |  -28.80876   4.654747    -6.19   0.000    -37.93526   -19.68226
            _cons |   58588.42   9275.608     6.32   0.000     40401.85    76774.99
-----------------------------------------------------------------------------------

reg_jahrend_7[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .23350013   .02456816   9.5041775   3.763e-21   .18532965    .2816706        3281   1.9606873           0
     jahrend  -28.808759   4.6547472  -6.1891135   6.799e-10  -37.935262  -19.682255        3281   1.9606873           0
       _cons    58588.42   9275.6081   6.3163966   3.038e-10   40401.853   76774.986        3281   1.9606873           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_jahrend_7.xlsx saved

Linear regression                               Number of obs     =      3,284
                                                F(4, 3279)        =      56.25
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1176
                                                Root MSE          =     429.99

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .1805022   .0232243     7.77   0.000     .1349665    .2260378
                  |
          decades |
           1990s  |  -694.2541   60.66837   -11.44   0.000    -813.2059   -575.3024
           2000s  |   -766.828   113.9359    -6.73   0.000    -990.2206   -543.4353
           2010s  |   -1454.42   497.5351    -2.92   0.003    -2429.931   -478.9089
                  |
            _cons |     1961.9   82.33097    23.83   0.000     1800.474    2123.325
-----------------------------------------------------------------------------------

reg_idecades_7[6,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .18050219   .02322433   7.7721154   1.025e-14   .13496652   .22603785        3279   1.9606877           0
  1b.decades           0           .           .           .           .           .        3279   1.9606877           0
   2.decades  -694.25414   60.668374  -11.443427   9.244e-30  -813.20587   -575.3024        3279   1.9606877           0
   3.decades  -766.82795   113.93586  -6.7303476   1.990e-11   -990.2206  -543.43531        3279   1.9606877           0
   4.decades  -1454.4199   497.53513  -2.9232507   .00348769  -2429.9309  -478.90889        3279   1.9606877           0
       _cons   1961.8996   82.330965   23.829425   6.89e-116   1800.4743   2123.3249        3279   1.9606877           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_idecades_7.xlsx saved

Linear regression                               Number of obs     =      3,284
                                                F(2, 3281)        =      91.11
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1061
                                                Root MSE          =     432.65

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .1887667   .0236139     7.99   0.000     .1424671    .2350663
          decades |  -543.5358   55.58851    -9.78   0.000    -652.5275   -434.5441
            _cons |   2345.405   126.5994    18.53   0.000     2097.183    2593.626
-----------------------------------------------------------------------------------

reg_decades_7[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .18876671   .02361395   7.9938648   1.795e-15   .14246714   .23506627        3281   1.9606873           0
     decades   -543.5358   55.588509  -9.7778446   2.816e-22  -652.52749  -434.54412        3281   1.9606873           0
       _cons   2345.4045   126.59937   18.526194   5.905e-73   2097.1827   2593.6263        3281   1.9606873           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_decades_7.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(18, 68)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.2643
                                                Root MSE          =     667.42

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .2043159   .1661516     1.23   0.223    -.1272345    .5358662
                  |
          jahrend |
            1980  |  -418.6783   204.0584    -2.05   0.044    -825.8705   -11.48616
            1981  |  -749.1246    170.626    -4.39   0.000    -1089.603   -408.6458
            1982  |  -355.2687    197.642    -1.80   0.077    -749.6572    39.11986
            1983  |  -644.5284   127.6326    -5.05   0.000    -899.2154   -389.8415
            1984  |  -24.94522   187.6097    -0.13   0.895    -399.3146    349.4242
            1985  |  -351.7107   145.9496    -2.41   0.019    -642.9487    -60.4728
            1986  |  -96.42181   19.15106    -5.03   0.000    -134.6372   -58.20646
            1987  |  -355.8703   215.3513    -1.65   0.103    -785.5971    73.85648
            1988  |   -460.976   410.2997    -1.12   0.265    -1279.716    357.7643
            1989  |  -498.1077   10.35154   -48.12   0.000    -518.7638   -477.4515
            1990  |  -210.4671   175.2859    -1.20   0.234    -560.2447    139.3104
            1991  |  -327.8037   158.9334    -2.06   0.043    -644.9504   -10.65701
            1992  |   -669.015   398.9189    -1.68   0.098    -1465.045    127.0152
            1996  |  -450.9081   128.1748    -3.52   0.001     -706.677   -195.1392
            2002  |  -266.8324   122.4921    -2.18   0.033    -511.2616   -22.40314
            2007  |  -1441.899   120.8489   -11.93   0.000     -1683.05   -1200.749
            2008  |  -935.5631   32.97326   -28.37   0.000     -1001.36   -869.7659
            2009  |  -1240.079   222.6432    -5.57   0.000    -1684.357   -795.8017
            2010  |  -570.3087   108.3201    -5.27   0.000    -786.4581   -354.1592
            2013  |  -1115.673   142.1109    -7.85   0.000    -1399.251   -832.0951
            2014  |  -238.7787   339.9482    -0.70   0.485    -917.1347    439.5773
            2015  |  -1102.335   309.1355    -3.57   0.001    -1719.205   -485.4647
            2016  |   435.8335   691.9862     0.63   0.531    -945.0034     1816.67
            2017  |  -654.3586   54.79114   -11.94   0.000    -763.6926   -545.0245
                  |
            _cons |   2094.937   359.9701     5.82   0.000     1376.628    2813.247
-----------------------------------------------------------------------------------

reg_ijahrend_8[27,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .20431585   .16615158   1.2296955   .22304887  -.12723445   .53586616          NA   1.9954689           0
1977b.jahr~d           0           .           .           .           .           .          NA   1.9954689           0
1980.jahrend  -418.67831   204.05838  -2.0517575    .0440467  -825.87046   -11.48616          NA   1.9954689           0
1981.jahrend  -749.12458   170.62596  -4.3904489   .00004048  -1089.6034  -408.64577          NA   1.9954689           0
1982.jahrend  -355.26868   197.64203   -1.797536   .07669034  -749.65722   39.119861          NA   1.9954689           0
1983.jahrend  -644.52843   127.63264  -5.0498716   3.536e-06   -899.2154  -389.84147          NA   1.9954689           0
1984.jahrend  -24.945223   187.60972  -.13296338   .89461503   -399.3146   349.42415          NA   1.9954689           0
1985.jahrend  -351.71072   145.94962  -2.4098092   .01867236  -642.94865  -60.472798          NA   1.9954689           0
1986.jahrend  -96.421807    19.15106  -5.0348027   3.744e-06  -134.63715  -58.206462          NA   1.9954689           0
1987.jahrend   -355.8703   215.35127  -1.6525108   .10304088  -785.59707    73.85648          NA   1.9954689           0
1988.jahrend  -460.97598   410.29971  -1.1235104   .26517165  -1279.7163   357.76435          NA   1.9954689           0
1989.jahrend  -498.10765   10.351535  -48.119206   2.954e-54  -518.76382  -477.45149          NA   1.9954689           0
1990.jahrend  -210.46715   175.28591  -1.2007077   .23403057  -560.24474   139.31045          NA   1.9954689           0
1991.jahrend  -327.80371   158.93342  -2.0625222   .04298066  -644.95041  -10.657007          NA   1.9954689           0
1992.jahrend  -669.01502   398.91887  -1.6770704   .09812025  -1465.0452    127.0152          NA   1.9954689           0
1996.jahrend  -450.90811   128.17484  -3.5179143   .00078019  -706.67703   -195.1392          NA   1.9954689           0
2002.jahrend  -266.83237   122.49213  -2.1783634   .03284953   -511.2616  -22.403136          NA   1.9954689           0
2007.jahrend  -1441.8995    120.8489  -11.931424   2.458e-18  -1683.0497  -1200.7492          NA   1.9954689           0
2008.jahrend  -935.56306   32.973259  -28.373388   2.047e-39  -1001.3602  -869.76595          NA   1.9954689           0
2009.jahrend  -1240.0794   222.64321  -5.5698053   4.718e-07   -1684.357  -795.80174          NA   1.9954689           0
2010.jahrend  -570.30868   108.32012    -5.26503   1.550e-06  -786.45811  -354.15925          NA   1.9954689           0
2013.jahrend   -1115.673   142.11091  -7.8507203   4.091e-11   -1399.251  -832.09512          NA   1.9954689           0
2014.jahrend  -238.77871   339.94817  -.70239742   .48482888  -917.13472   439.57729          NA   1.9954689           0
2015.jahrend   -1102.335   309.13552  -3.5658632   .00066935  -1719.2053  -485.46465          NA   1.9954689           0
2016.jahrend    435.8335   691.98616   .62982979   .53091661  -945.00339   1816.6704          NA   1.9954689           0
2017.jahrend  -654.35857   54.791139  -11.942781   2.352e-18  -763.69258  -545.02455          NA   1.9954689           0
       _cons   2094.9374   359.97011   5.8197539   1.751e-07   1376.6283   2813.2466          NA   1.9954689           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_ijahrend_8.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(2, 91)          =       0.57
                                                Prob > F          =     0.5702
                                                R-squared         =     0.0368
                                                Root MSE          =     660.14

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .1608324   .1641765     0.98   0.330     -.165284    .4869487
          jahrend |  -8.287615   8.312017    -1.00   0.321    -24.79842    8.223185
            _cons |   18273.27   16243.53     1.12   0.264     -13992.5    50539.05
-----------------------------------------------------------------------------------

reg_jahrend_8[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .16083237   .16417646   .97963113   .32986549    -.165284   .48694874          NA   1.9863772           0
     jahrend  -8.2876153   8.3120169   -.9970643   .32137722  -24.798416   8.2231852          NA   1.9863772           0
       _cons   18273.271   16243.529    1.124957   .26356572  -13992.503   50539.045          NA   1.9863772           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_jahrend_8.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(4, 89)          =       2.11
                                                Prob > F          =     0.0865
                                                R-squared         =     0.0716
                                                Root MSE          =     655.34

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .1818281   .1549131     1.17   0.244    -.1259809    .4896372
                  |
          decades |
           1990s  |   -213.573   238.0848    -0.90   0.372    -686.6423    259.4964
           2000s  |  -659.7253   227.6907    -2.90   0.005    -1112.142   -207.3087
           2010s  |  -199.0949   253.2425    -0.79   0.434    -702.2823    304.0925
                  |
            _cons |   1795.296    362.084     4.96   0.000     1075.843    2514.749
-----------------------------------------------------------------------------------

reg_idecades_8[6,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .18182812    .1549131   1.1737427   .24362955  -.12598092   .48963716          NA   1.9869787           0
  1b.decades           0           .           .           .           .           .          NA   1.9869787           0
   2.decades  -213.57297   238.08477  -.89704592   .37211455  -686.64233   259.49639          NA   1.9869787           0
   3.decades  -659.72528   227.69069  -2.8974626   .00473496  -1112.1418  -207.30872          NA   1.9869787           0
   4.decades  -199.09492   253.24247  -.78618295   .43384856  -702.28232   304.09248          NA   1.9869787           0
       _cons   1795.2957   362.08397   4.9582303   3.379e-06   1075.8426   2514.7489          NA   1.9869787           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_idecades_8.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(2, 91)          =       0.75
                                                Prob > F          =     0.4773
                                                R-squared         =     0.0418
                                                Root MSE          =     658.43

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .1556781    .157044     0.99   0.324    -.1562705    .4676268
          decades |  -95.58982    80.1793    -1.19   0.236    -254.8562    63.67652
            _cons |   1940.127   327.6908     5.92   0.000     1289.209    2591.044
-----------------------------------------------------------------------------------

reg_decades_8[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .15567814   .15704401   .99130265    .3241664  -.15627049   .46762676          NA   1.9863772           0
     decades  -95.589816   80.179305  -1.1922006   .23628227  -254.85615   63.676523          NA   1.9863772           0
       _cons   1940.1266   327.69083   5.9206008   5.609e-08    1289.209   2591.0442          NA   1.9863772           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_decades_8.xlsx saved

Linear regression                               Number of obs     =      4,575
                                                F(26, 4543)       =          .
                                                Prob > F          =          .
                                                R-squared         =     0.1310
                                                Root MSE          =     460.17

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .2396967   .0222961    10.75   0.000     .1959855    .2834079
                  |
          jahrend |
            1979  |   1328.193   34.32177    38.70   0.000     1260.906    1395.481
            1980  |   2402.383   847.6146     2.83   0.005     740.6458    4064.119
            1981  |    683.065   149.6217     4.57   0.000     389.7338    976.3963
            1982  |   519.7292   291.7106     1.78   0.075    -52.16548    1091.624
            1983  |   342.8907   456.5083     0.75   0.453    -552.0876    1237.869
            1985  |   63.90981   245.8511     0.26   0.795    -418.0778    545.8974
            1986  |   563.1819   209.8039     2.68   0.007     151.8643    974.4995
            1987  |   572.3919   142.2144     4.02   0.000     293.5825    851.2014
            1989  |   567.1424   178.1809     3.18   0.001     217.8211    916.4637
            1991  |  -171.9014   21.16624    -8.12   0.000    -213.3975   -130.4053
            1992  |  -269.7495   28.19853    -9.57   0.000    -325.0323   -214.4666
            1993  |  -334.9791   19.32304   -17.34   0.000    -372.8617   -297.0966
            1994  |  -406.1784   24.45111   -16.61   0.000    -454.1145   -358.2424
            1995  |   -326.657   28.69081   -11.39   0.000    -382.9049    -270.409
            1996  |  -298.9766   29.64693   -10.08   0.000     -357.099   -240.8542
            1997  |  -335.6261    57.7526    -5.81   0.000    -448.8492   -222.4029
            1998  |  -259.8901   154.3246    -1.68   0.092    -562.4414    42.66117
            1999  |  -345.6988    107.485    -3.22   0.001    -556.4217   -134.9759
            2000  |   -393.391   135.7382    -2.90   0.004    -659.5039   -127.2782
            2001  |  -485.5703   226.2385    -2.15   0.032    -929.1078   -42.03276
            2002  |  -143.7531   148.6077    -0.97   0.333    -435.0965    147.5904
            2003  |   103.4818   192.7706     0.54   0.591    -274.4423     481.406
            2004  |  -588.1956    147.736    -3.98   0.000      -877.83   -298.5612
            2005  |   -823.032    27.9968   -29.40   0.000    -877.9193   -768.1447
            2006  |  -573.0751   80.83077    -7.09   0.000    -731.5427   -414.6075
            2007  |  -435.3676   171.0917    -2.54   0.011    -770.7905   -99.94462
            2008  |   48.73807    324.072     0.15   0.880    -586.6007    684.0769
            2009  |   -451.556   29.59888   -15.26   0.000    -509.5842   -393.5278
            2010  |    528.903   357.0244     1.48   0.139    -171.0385    1228.844
            2011  |  -738.6746   146.3991    -5.05   0.000    -1025.688   -451.6612
                  |
            _cons |   1465.161    69.8327    20.98   0.000     1328.255    1602.067
-----------------------------------------------------------------------------------

reg_ijahrend_9[33,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .23969668   .02229609   10.750614   1.228e-26    .1959855   .28340786        4543   1.9604863           0
1976b.jahr~d           0           .           .           .           .           .        4543   1.9604863           0
1979.jahrend   1328.1935   34.321769   38.698282   2.06e-283   1260.9061   1395.4809        4543   1.9604863           0
1980.jahrend   2402.3826   847.61456   2.8342866   .00461308   740.64585   4064.1193        4543   1.9604863           0
1981.jahrend   683.06503   149.62168    4.565281   5.120e-06   389.73377   976.39629        4543   1.9604863           0
1982.jahrend   519.72917   291.71061   1.7816602   .07487145   -52.16548   1091.6238        4543   1.9604863           0
1983.jahrend   342.89069   456.50833   .75111594   .45262179  -552.08765    1237.869        4543   1.9604863           0
1985.jahrend   63.909811   245.85106   .25995337   .79491153  -418.07783   545.89745        4543   1.9604863           0
1986.jahrend   563.18187   209.80386   2.6843255     .007294   151.86426   974.49947        4543   1.9604863           0
1987.jahrend   572.39193   142.21442   4.0248514   .00005794    293.5825   851.20136        4543   1.9604863           0
1989.jahrend    567.1424   178.18094   3.1829577   .00146762   217.82111    916.4637        4543   1.9604863           0
1991.jahrend  -171.90141   21.166242  -8.1214897   5.879e-16  -213.39754  -130.40529        4543   1.9604863           0
1992.jahrend  -269.74948   28.198533   -9.566082   1.767e-21  -325.03231  -214.46664        4543   1.9604863           0
1993.jahrend  -334.97913   19.323044  -17.335733   3.051e-65  -372.86169  -297.09656        4543   1.9604863           0
1994.jahrend  -406.17844   24.451105  -16.611864   3.308e-60   -454.1145  -358.24239        4543   1.9604863           0
1995.jahrend  -326.65697   28.690809  -11.385422   1.242e-29   -382.9049  -270.40903        4543   1.9604863           0
1996.jahrend  -298.97656    29.64693  -10.084571   1.145e-23  -357.09896  -240.85416        4543   1.9604863           0
1997.jahrend  -335.62606   57.752602  -5.8114448   6.617e-09  -448.84924  -222.40287        4543   1.9604863           0
1998.jahrend  -259.89012   154.32461  -1.6840484   .09224101   -562.4414   42.661167        4543   1.9604863           0
1999.jahrend  -345.69878   107.48502  -3.2162508   .00130788  -556.42169  -134.97587        4543   1.9604863           0
2000.jahrend  -393.39104   135.73818  -2.8981606   .00377155  -659.50388   -127.2782        4543   1.9604863           0
2001.jahrend  -485.57029   226.23853  -2.1462759   .03190378  -929.10782  -42.032758        4543   1.9604863           0
2002.jahrend  -143.75309   148.60774  -.96733241   .33342938  -435.09653   147.59036        4543   1.9604863           0
2003.jahrend   103.48183   192.77062   .53681329   .59142288  -274.44233   481.40598        4543   1.9604863           0
2004.jahrend   -588.1956   147.73602   -3.981396   .00006958  -877.83005  -298.56116        4543   1.9604863           0
2005.jahrend  -823.03199   27.996799  -29.397361   4.76e-174  -877.91933  -768.14465        4543   1.9604863           0
2006.jahrend  -573.07509   80.830769  -7.0898137   1.550e-12   -731.5427  -414.60747        4543   1.9604863           0
2007.jahrend  -435.36757   171.09171  -2.5446445   .01097171  -770.79051  -99.944621        4543   1.9604863           0
2008.jahrend    48.73807   324.07204    .1503927   .88046148  -586.60073   684.07687        4543   1.9604863           0
2009.jahrend  -451.55599   29.598876   -15.25585   2.757e-51  -509.58419   -393.5278        4543   1.9604863           0
2010.jahrend   528.90296   357.02442   1.4814196   .13856415  -171.03852   1228.8444        4543   1.9604863           0
2011.jahrend  -738.67464   146.39911  -5.0456224   4.697e-07  -1025.6881  -451.66119        4543   1.9604863           0
       _cons   1465.1608   69.832704   20.981012   2.304e-93   1328.2548   1602.0669        4543   1.9604863           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_ijahrend_9.xlsx saved

Linear regression                               Number of obs     =      4,575
                                                F(2, 4572)        =      72.46
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0881
                                                Root MSE          =     469.89

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .2327655   .0206174    11.29   0.000     .1923455    .2731856
          jahrend |  -14.27016   5.016311    -2.84   0.004    -24.10456   -4.435771
            _cons |    29623.2      10007     2.96   0.003     10004.65    49241.75
-----------------------------------------------------------------------------------

reg_jahrend_9[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .23276553    .0206174   11.289764   3.575e-29   .19234548   .27318559        4572    1.960483           0
     jahrend  -14.270164   5.0163115  -2.8447524   .00446435  -24.104557  -4.4357707        4572    1.960483           0
       _cons   29623.198   10006.999    2.960248   .00308971   10004.647   49241.749        4572    1.960483           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_jahrend_9.xlsx saved

Linear regression                               Number of obs     =      4,575
                                                F(4, 4570)        =      45.59
                                                Prob > F          =     0.0000
                                                R-squared         =     0.1099
                                                Root MSE          =     464.36

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .2197611   .0198871    11.05   0.000     .1807728    .2587493
                  |
          decades |
           1990s  |  -903.4322   131.6479    -6.86   0.000    -1161.526   -645.3388
           2000s  |  -851.8942   152.9668    -5.57   0.000    -1151.783   -552.0053
           2010s  |  -568.6618   382.0662    -1.49   0.137    -1317.696    180.3727
                  |
            _cons |    2096.92   141.1553    14.86   0.000     1820.187    2373.653
-----------------------------------------------------------------------------------

reg_idecades_9[6,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .21976105   .01988709    11.05044   4.926e-28   .18077275   .25874935        4570   1.9604832           0
  1b.decades           0           .           .           .           .           .        4570   1.9604832           0
   2.decades  -903.43224   131.64786  -6.8624911   7.672e-12  -1161.5257  -645.33883        4570   1.9604832           0
   3.decades  -851.89419   152.96681  -5.5691439   2.707e-08  -1151.7831  -552.00532        4570   1.9604832           0
   4.decades  -568.66176   382.06625  -1.4883852   .13671833  -1317.6962   180.37271        4570   1.9604832           0
       _cons   2096.9201   141.15534   14.855408   8.683e-49   1820.1875   2373.6528        4570   1.9604832           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_idecades_9.xlsx saved

Linear regression                               Number of obs     =      4,575
                                                F(2, 4572)        =      71.17
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0875
                                                Root MSE          =     470.06

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .2336896   .0205344    11.38   0.000     .1934322    .2739469
          decades |  -161.2602   72.36759    -2.23   0.026    -303.1356   -19.38477
            _cons |   1494.482   157.7243     9.48   0.000     1185.266    1803.697
-----------------------------------------------------------------------------------

reg_decades_9[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .23368955   .02053443   11.380379   1.306e-29   .19343216   .27394695        4572    1.960483           0
     decades   -161.2602   72.367592  -2.2283484   .02590574  -303.13564  -19.384772        4572    1.960483           0
       _cons   1494.4815   157.72431   9.4752773   4.151e-21   1185.2657   1803.6974        4572    1.960483           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_decades_9.xlsx saved

Linear regression                               Number of obs     =        193
                                                F(7, 176)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.2273
                                                Root MSE          =     591.45

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |    .383466   .1809505     2.12   0.035      .026354    .7405781
                  |
          jahrend |
            1981  |  -346.0415   335.4738    -1.03   0.304     -1008.11    316.0275
            1982  |   5.658294   130.3221     0.04   0.965    -251.5368    262.8534
            1983  |  -31.44997   150.9538    -0.21   0.835    -329.3624    266.4625
            1985  |   274.3966   105.4962     2.60   0.010     66.19615    482.5971
            1988  |   317.6612   100.5986     3.16   0.002     119.1264     516.196
            1991  |   493.9165   191.6109     2.58   0.011     115.7658    872.0672
            1992  |   120.1335   189.6188     0.63   0.527    -254.0858    494.3527
            1995  |   172.7597   116.6329     1.48   0.140    -57.41933    402.9387
            2002  |  -203.1836   97.36486    -2.09   0.038    -395.3365   -11.03068
            2003  |   184.4251   259.9292     0.71   0.479    -328.5541    697.4042
            2008  |  -20.97437   331.1397    -0.06   0.950    -674.4899    632.5411
            2009  |   19.80667   101.3846     0.20   0.845    -180.2794    219.8927
            2010  |   237.3491   100.1527     2.37   0.019     39.69428     435.004
            2012  |  -270.1881   250.4986    -1.08   0.282    -764.5558    224.1795
            2014  |   548.9334   836.2706     0.66   0.512    -1101.475    2199.342
                  |
            _cons |   1041.208     514.09     2.03   0.044     26.63409    2055.783
-----------------------------------------------------------------------------------

reg_ijahrend_10[18,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .38346605   .18095053   2.1191762   .03547814   .02635396   .74057814         176   1.9735344           0
1980b.jahr~d           0           .           .           .           .           .         176   1.9735344           0
1981.jahrend  -346.04145   335.47376  -1.0315008    .3037214  -1008.1105   316.02754         176   1.9735344           0
1982.jahrend   5.6582936   130.32206   .04341777   .96541773  -251.53677   262.85335         176   1.9735344           0
1983.jahrend  -31.449965   150.95375  -.20834172   .83520306  -329.36239   266.46246         176   1.9735344           0
1985.jahrend   274.39662   105.49625   2.6010084   .01008576   66.196148   482.59709         176   1.9735344           0
1988.jahrend   317.66117    100.5986   3.1577095   .00187146   119.12637   516.19598         176   1.9735344           0
1991.jahrend   493.91647    191.6109   2.5777055   .01076464   115.76576   872.06717         176   1.9735344           0
1992.jahrend   120.13345   189.61883   .63355235   .52719546  -254.08582   494.35273         176   1.9735344           0
1995.jahrend   172.75968   116.63289   1.4812261   .14033439  -57.419334   402.93869         176   1.9735344           0
2002.jahrend  -203.18357   97.364856  -2.0868266   .03834478  -395.33647  -11.030683         176   1.9735344           0
2003.jahrend   184.42507   259.92918   .70952048   .47894046   -328.5541   697.40425         176   1.9735344           0
2008.jahrend  -20.974366   331.13966  -.06333994   .94956769  -674.48987   632.54114         176   1.9735344           0
2009.jahrend   19.806666   101.38464   .19536159   .84533524  -180.27942   219.89275         176   1.9735344           0
2010.jahrend   237.34912   100.15272   2.3698719    .0188767   39.694279   435.00396         176   1.9735344           0
2012.jahrend  -270.18812   250.49863  -1.0786012   .28224157  -764.55578   224.17954         176   1.9735344           0
2014.jahrend   548.93335   836.27058   .65640639   .51241998  -1101.4754   2199.3421         176   1.9735344           0
       _cons   1041.2083   514.08998   2.0253426   .04434285   26.634087   2055.7826         176   1.9735344           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_ijahrend_10.xlsx saved

Linear regression                               Number of obs     =        193
                                                F(2, 190)         =       7.23
                                                Prob > F          =     0.0009
                                                R-squared         =     0.1757
                                                Root MSE          =     587.93

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .3867236   .1290233     3.00   0.003     .1322216    .6412256
          jahrend |   12.83499   5.593385     2.29   0.023     1.801878     23.8681
            _cons |  -24397.36   11104.64    -2.20   0.029    -46301.56   -2493.148
-----------------------------------------------------------------------------------

reg_jahrend_10[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .38672359   .12902327   2.9973167   .00308755   .13222156   .64122562         190   1.9725282           0
     jahrend   12.834987   5.5933849   2.2946726   .02284488   1.8018779   23.868097         190   1.9725282           0
       _cons  -24397.355   11104.636  -2.1970423   .02922692  -46301.562  -2493.1479         190   1.9725282           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_jahrend_10.xlsx saved

Linear regression                               Number of obs     =        193
                                                F(4, 188)         =       6.55
                                                Prob > F          =     0.0001
                                                R-squared         =     0.1625
                                                Root MSE          =     595.76

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .4107285   .1472226     2.79   0.006     .1203079    .7011491
                  |
          decades |
           1990s  |    98.7343    134.379     0.73   0.463    -166.3501    363.8187
           2000s  |   44.12159   149.7359     0.29   0.769    -251.2569    339.5001
           2010s  |   93.04589   311.5151     0.30   0.766    -521.4684    707.5601
                  |
            _cons |   989.6267   453.0685     2.18   0.030      95.8753    1883.378
-----------------------------------------------------------------------------------

reg_idecades_10[6,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .41072851   .14722263   2.7898462   .00581662   .12030791    .7011491         188   1.9726627           0
  1b.decades           0           .           .           .           .           .         188   1.9726627           0
   2.decades   98.734297     134.379   .73474499   .46341031  -166.35014   363.81874         188   1.9726627           0
   3.decades   44.121586   149.73594   .29466264   .76857673  -251.25691   339.50008         188   1.9726627           0
   4.decades   93.045888   311.51511    .2986882   .76550785  -521.46835   707.56013         188   1.9726627           0
       _cons   989.62669   453.06853   2.1842759   .03017949   95.875304   1883.3781         188   1.9726627           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_idecades_10.xlsx saved

Linear regression                               Number of obs     =        193
                                                F(2, 190)         =       8.94
                                                Prob > F          =     0.0002
                                                R-squared         =     0.1622
                                                Root MSE          =     592.74

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .4093865   .1361006     3.01   0.003     .1409243    .6778487
          decades |    33.2505   59.87665     0.56   0.579    -84.85789    151.3589
            _cons |   961.8548   384.1179     2.50   0.013     204.1714    1719.538
-----------------------------------------------------------------------------------

reg_decades_10[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y    .4093865   .13610057   3.0079705   .00298614   .14092429   .67784871         190   1.9725282           0
     decades   33.250495   59.876652   .55531654   .57933139  -84.857889   151.35888         190   1.9725282           0
       _cons   961.85485   384.11791   2.5040614    .0131196   204.17144   1719.5383         190   1.9725282           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_decades_10.xlsx saved

Linear regression                               Number of obs     =        763
                                                F(19, 740)        =          .
                                                Prob > F          =          .
                                                R-squared         =     0.3574
                                                Root MSE          =     718.47

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .3710589   .1218358     3.05   0.002      .131874    .6102438
                  |
          jahrend |
            1985  |   2357.129   640.3748     3.68   0.000     1099.961    3614.297
            1986  |   2099.837   597.2152     3.52   0.000     927.3995    3272.275
            1988  |   4583.424   619.7062     7.40   0.000     3366.833    5800.016
            1992  |     2483.3   824.8222     3.01   0.003     864.0298     4102.57
            1993  |   2163.389   781.4628     2.77   0.006     629.2412    3697.538
            1994  |   2260.676   789.0719     2.86   0.004     711.5896    3809.762
            1995  |   2601.966   787.9631     3.30   0.001     1055.057    4148.876
            1996  |   2430.959   834.7718     2.91   0.004     792.1564    4069.762
            1997  |   2339.173   827.3279     2.83   0.005     714.9831    3963.362
            1998  |   2401.663   833.2461     2.88   0.004     765.8551    4037.471
            1999  |    2077.79    788.259     2.64   0.009        530.3    3625.281
            2000  |   2169.813   832.1677     2.61   0.009      536.122    3803.503
            2001  |    1503.73   819.2458     1.84   0.067    -104.5931    3112.053
            2002  |   2483.879   770.4746     3.22   0.001     971.3025    3996.455
            2003  |   2441.903    656.882     3.72   0.000     1152.329    3731.477
            2004  |   2767.682   1184.106     2.34   0.020     443.0751    5092.288
            2005  |   1982.745   836.4817     2.37   0.018     340.5851    3624.905
            2006  |   2672.787   979.1921     2.73   0.006     750.4616    4595.112
            2007  |    2702.13   880.9275     3.07   0.002     972.7155    4431.545
            2008  |   1896.122   969.9338     1.95   0.051    -8.027661    3800.272
            2012  |   2486.449   706.8364     3.52   0.000     1098.805    3874.092
                  |
            _cons |  -1486.362   1087.007    -1.37   0.172    -3620.347    647.6226
-----------------------------------------------------------------------------------

reg_ijahrend_11[24,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .37105892   .12183577   3.0455664   .00240486     .131874   .61024384         740   1.9631749           0
1982b.jahr~d           0           .           .           .           .           .         740   1.9631749           0
1985.jahrend   2357.1291   640.37483   3.6808585   .00024934   1099.9613   3614.2969         740   1.9631749           0
1986.jahrend   2099.8373   597.21516   3.5160482   .00046471   927.39949   3272.2751         740   1.9631749           0
1988.jahrend   4583.4244   619.70622   7.3961246   3.808e-13   3366.8327   5800.0161         740   1.9631749           0
1992.jahrend   2483.3001   824.82221   3.0107095   .00269503   864.02978   4102.5703         740   1.9631749           0
1993.jahrend   2163.3894   781.46281   2.7683843    .0057744    629.2412   3697.5376         740   1.9631749           0
1994.jahrend   2260.6758    789.0719   2.8649807   .00428854   711.58963   3809.7619         740   1.9631749           0
1995.jahrend   2601.9664   787.96308   3.3021425   .00100557    1055.057   4148.8757         740   1.9631749           0
1996.jahrend   2430.9595   834.77177   2.9121246    .0036978   792.15645   4069.7625         740   1.9631749           0
1997.jahrend   2339.1725   827.32793   2.8273825   .00481976   714.98307    3963.362         740   1.9631749           0
1998.jahrend   2401.6629   833.24611   2.8822972   .00406226   765.85506   4037.4708         740   1.9631749           0
1999.jahrend   2077.7904   788.25902   2.6359233   .00856667   530.30002   3625.2807         740   1.9631749           0
2000.jahrend   2169.8127   832.16768   2.6074225   .00930649     536.122   3803.5034         740   1.9631749           0
2001.jahrend   1503.7297   819.24581   1.8355049   .06683205  -104.59311   3112.0525         740   1.9631749           0
2002.jahrend   2483.8789   770.47463   3.2238296   .00132039   971.30245   3996.4554         740   1.9631749           0
2003.jahrend   2441.9028   656.88203   3.7174146   .00021646   1152.3285   3731.4772         740   1.9631749           0
2004.jahrend   2767.6817   1184.1057   2.3373604   .01968618   443.07511   5092.2883         740   1.9631749           0
2005.jahrend    1982.745   836.48167   2.3703388   .01802718   340.58512   3624.9048         740   1.9631749           0
2006.jahrend   2672.7869   979.19206    2.729584   .00649212   750.46164   4595.1122         740   1.9631749           0
2007.jahrend   2702.1302   880.92746   3.0673696   .00223822   972.71546   4431.5448         740   1.9631749           0
2008.jahrend    1896.122   969.93378   1.9548984   .05097118  -8.0276612   3800.2717         740   1.9631749           0
2012.jahrend    2486.449   706.83639    3.517715   .00046185   1098.8055   3874.0924         740   1.9631749           0
       _cons  -1486.3623    1087.007  -1.3673898   .17191833  -3620.3473   647.62264         740   1.9631749           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_ijahrend_11.xlsx saved

Linear regression                               Number of obs     =        763
                                                F(2, 760)         =       5.90
                                                Prob > F          =     0.0029
                                                R-squared         =     0.2624
                                                Root MSE          =     759.54

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .3810232   .1154241     3.30   0.001     .1544353     .607611
          jahrend |  -1.684729   10.47551    -0.16   0.872     -22.2491    18.87964
            _cons |   4189.355   20960.87     0.20   0.842    -36958.73    45337.44
-----------------------------------------------------------------------------------

reg_jahrend_11[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .38102318   .11542405   3.3010727   .00100812   .15443535   .60761102         760   1.9630903           0
     jahrend  -1.6847291   10.475507  -.16082553   .87227355  -22.249096   18.879638         760   1.9630903           0
       _cons   4189.3545   20960.872   .19986547   .84163926   -36958.73   45337.439         760   1.9630903           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_jahrend_11.xlsx saved

Linear regression                               Number of obs     =        763
                                                F(4, 758)         =       5.73
                                                Prob > F          =     0.0002
                                                R-squared         =     0.2725
                                                Root MSE          =      755.3

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .3661495   .1170555     3.13   0.002      .136358    .5959411
                  |
          decades |
           1990s  |  -932.7971   709.8742    -1.31   0.189     -2326.35    460.7559
           2000s  |   -975.697   731.8711    -1.33   0.183    -2412.432    461.0381
           2010s  |  -768.3191   847.7743    -0.91   0.365    -2432.584    895.9454
                  |
            _cons |   1793.903    850.461     2.11   0.035     124.3643    3463.442
-----------------------------------------------------------------------------------

reg_idecades_11[6,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .36614955   .11705552   3.1279991   .00182734   .13635804   .59594106         758   1.9630985           0
  1b.decades           0           .           .           .           .           .         758   1.9630985           0
   2.decades  -932.79714    709.8742  -1.3140316   .18923308  -2326.3502   460.75587         758   1.9630985           0
   3.decades  -975.69697   731.87109  -1.3331541   .18288175  -2412.4321    461.0381         758   1.9630985           0
   4.decades  -768.31913   847.77434  -.90627788   .36507678  -2432.5837   895.94544         758   1.9630985           0
       _cons   1793.9031     850.461   2.1093302   .03524342   124.36434   3463.4419         758   1.9630985           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_idecades_11.xlsx saved

Linear regression                               Number of obs     =        763
                                                F(2, 760)         =       6.95
                                                Prob > F          =     0.0010
                                                R-squared         =     0.2644
                                                Root MSE          =     758.53

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .3843758   .1153445     3.33   0.001     .1579441    .6108075
          decades |  -118.1048   121.5799    -0.97   0.332    -356.7772    120.5676
            _cons |    1068.53   409.3723     2.61   0.009     264.8955    1872.165
-----------------------------------------------------------------------------------

reg_decades_11[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .38437582    .1153445   3.3324156   .00090261   .15794415   .61080749         760   1.9630903           0
     decades  -118.10479   121.57992  -.97141687   .33164979  -356.77715   120.56758         760   1.9630903           0
       _cons   1068.5303   409.37231   2.6101675   .00922799   264.89548   1872.1651         760   1.9630903           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_decades_11.xlsx saved

Linear regression                               Number of obs     =        102
                                                F(8, 89)          =          .
                                                Prob > F          =          .
                                                R-squared         =     0.5345
                                                Root MSE          =     1476.3

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .2466492   .1542642     1.60   0.113    -.0598704    .5531688
                  |
          jahrend |
            1982  |   222.9767   106.2982     2.10   0.039     11.76446    434.1889
            1983  |   367.6379   165.7554     2.22   0.029     38.28552    696.9903
            1986  |   52.88428   137.6777     0.38   0.702    -220.6783    326.4469
            1991  |   393.0126     137.59     2.86   0.005     119.6242    666.4011
            1992  |   420.5342   87.80233     4.79   0.000     246.0728    594.9956
            2008  |   2621.113    16.6536   157.39   0.000     2588.023    2654.204
            2010  |  -3097.035   1807.735    -1.71   0.090    -6688.966    494.8964
            2013  |   2829.224   1168.349     2.42   0.017     507.7396    5150.709
            2014  |   -338.845   668.0605    -0.51   0.613    -1666.267     988.577
            2015  |   7952.576   4520.342     1.76   0.082    -1029.247     16934.4
            2017  |  -229.9677   1340.314    -0.17   0.864    -2893.144    2433.208
                  |
            _cons |   1170.317   575.9878     2.03   0.045     25.84167    2314.793
-----------------------------------------------------------------------------------

reg_ijahrend_12[14,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .24664923   .15426416   1.5988757   .11339251  -.05987038   .55316884          NA   1.9869787           0
1981b.jahr~d           0           .           .           .           .           .          NA   1.9869787           0
1982.jahrend   222.97669   106.29819   2.0976529   .03877107   11.764464   434.18892          NA   1.9869787           0
1983.jahrend    367.6379   165.75537   2.2179548   .02910356   38.285521   696.99029          NA   1.9869787           0
1986.jahrend   52.884283   137.67768   .38411662   .70180821  -220.67833   326.44689          NA   1.9869787           0
1991.jahrend   393.01264   137.59004   2.8564032   .00533264   119.62416   666.40113          NA   1.9869787           0
1992.jahrend   420.53419   87.802334   4.7895559   6.628e-06   246.07282   594.99556          NA   1.9869787           0
2008.jahrend   2621.1132     16.6536   157.39019   1.19e-110   2588.0228   2654.2035          NA   1.9869787           0
2010.jahrend   -3097.035   1807.7352  -1.7132127   .09015492  -6688.9664   494.89638          NA   1.9869787           0
2013.jahrend   2829.2241    1168.349   2.4215574   .01748322   507.73958   5150.7086          NA   1.9869787           0
2014.jahrend  -338.84503   668.06053  -.50720707   .61326403  -1666.2671   988.57702          NA   1.9869787           0
2015.jahrend   7952.5758   4520.3417   1.7592864   .08196549  -1029.2468   16934.399          NA   1.9869787           0
2017.jahrend  -229.96772   1340.3142  -.17157747   .86415924  -2893.1435   2433.2081          NA   1.9869787           0
       _cons   1170.3171   575.98778   2.0318437   .04515133   25.841674   2314.7926          NA   1.9869787           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_ijahrend_12.xlsx saved

Linear regression                               Number of obs     =        102
                                                F(2, 99)          =       2.13
                                                Prob > F          =     0.1246
                                                R-squared         =     0.1959
                                                Root MSE          =     1839.7

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .0443063   .2179624     0.20   0.839    -.3881783    .4767909
          jahrend |   87.48696   60.00206     1.46   0.148    -31.57014    206.5441
            _cons |  -171356.7   118516.8    -1.45   0.151    -406519.9    63806.38
-----------------------------------------------------------------------------------

reg_jahrend_12[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .04430631   .21796237   .20327505   .83933728  -.38817831   .47679093          NA    1.984217           0
     jahrend   87.486962   60.002059    1.458066   .14798748   -31.57014   206.54406          NA    1.984217           0
       _cons  -171356.75   118516.84   -1.445843   .15137959  -406519.87   63806.377          NA    1.984217           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_jahrend_12.xlsx saved

Linear regression                               Number of obs     =        102
                                                F(2, 97)          =          .
                                                Prob > F          =          .
                                                R-squared         =     0.1985
                                                Root MSE          =     1855.5

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .0142968   .2477241     0.06   0.954    -.4773669    .5059605
                  |
          decades |
           1990s  |   111.7075   74.73395     1.49   0.138    -36.61871    260.0337
           2000s  |   2469.618   142.7862    17.30   0.000     2186.227    2753.009
           2010s  |   2903.035   2197.406     1.32   0.190    -1458.207    7264.277
                  |
            _cons |   2214.448    826.123     2.68   0.009     574.8225    3854.074
-----------------------------------------------------------------------------------

reg_idecades_12[6,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .01429678   .24772408   .05771252   .95409634  -.47736694   .50596051          NA   1.9847232           0
  1b.decades           0           .           .           .           .           .          NA   1.9847232           0
   2.decades   111.70749   74.733947   1.4947356   .13822965  -36.618707   260.03369          NA   1.9847232           0
   3.decades   2469.6179   142.78615    17.29592   2.135e-31   2186.2269   2753.0089          NA   1.9847232           0
   4.decades   2903.0351   2197.4056   1.3211194   .18956958  -1458.2067    7264.277          NA   1.9847232           0
       _cons    2214.448   826.12303   2.6805305   .00863816   574.82246   3854.0735          NA   1.9847232           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_idecades_12.xlsx saved

Linear regression                               Number of obs     =        102
                                                F(2, 99)          =       2.00
                                                Prob > F          =     0.1406
                                                R-squared         =     0.1961
                                                Root MSE          =     1839.5

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |    .015295   .2382751     0.06   0.949    -.4574944    .4880844
          decades |    970.553   682.5245     1.42   0.158    -383.7236     2324.83
            _cons |    1236.19   603.0042     2.05   0.043     39.69914    2432.681
-----------------------------------------------------------------------------------

reg_decades_12[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .01529502   .23827506    .0641906   .94894789  -.45749439   .48808443          NA    1.984217           0
     decades   970.55299   682.52446   1.4220047    .1581678  -383.72362   2324.8296          NA    1.984217           0
       _cons   1236.1902   603.00415   2.0500525   .04300277   39.699138   2432.6813          NA    1.984217           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_decades_12.xlsx saved

Linear regression                               Number of obs     =        136
                                                F(9, 118)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.3614
                                                Root MSE          =     406.55

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .2522678   .1262438     2.00   0.048     .0022707     .502265
                  |
          jahrend |
            1980  |   1519.742   5.546161   274.02   0.000     1508.759    1530.725
            1982  |   1374.475   273.6158     5.02   0.000     832.6416    1916.309
            1983  |  -53.12898   92.31605    -0.58   0.566    -235.9399    129.6819
            1985  |   768.7693   50.32457    15.28   0.000     669.1129    868.4256
            1986  |   1159.842   241.6482     4.80   0.000      681.313    1638.372
            1989  |   1192.778   208.7429     5.71   0.000     779.4104    1606.146
            1992  |   773.3775   142.1272     5.44   0.000     491.9271    1054.828
            1993  |   863.5443   111.0308     7.78   0.000     643.6731    1083.415
            1994  |    511.665   150.3609     3.40   0.001     213.9096    809.4205
            1995  |   828.3655   173.7601     4.77   0.000     484.2733    1172.458
            1996  |   1411.251   322.6373     4.37   0.000     772.3417    2050.161
            2000  |    1401.65   254.6999     5.50   0.000      897.275    1906.025
            2001  |   1798.642   418.8989     4.29   0.000     969.1082    2628.176
            2002  |   1282.671   247.4488     5.18   0.000     792.6547    1772.687
            2005  |   1739.392   4.014653   433.26   0.000     1731.442    1747.342
            2012  |   327.8537   128.6841     2.55   0.012     73.02409    582.6832
                  |
            _cons |  -98.27218   333.3125    -0.29   0.769    -758.3216    561.7772
-----------------------------------------------------------------------------------

reg_ijahrend_13[19,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .25226785   .12624384   1.9982587   .04798802   .00227068   .50226502         118   1.9802722           0
1979b.jahr~d           0           .           .           .           .           .         118   1.9802722           0
1980.jahrend    1519.742   5.5461605   274.01695   2.56e-167   1508.7591   1530.7249         118   1.9802722           0
1982.jahrend   1374.4753   273.61577   5.0233775   1.823e-06    832.6416    1916.309         118   1.9802722           0
1983.jahrend  -53.128985   92.316052  -.57551188   .56604103   -235.9399   129.68193         118   1.9802722           0
1985.jahrend   768.76927   50.324566   15.276222   9.853e-30   669.11293   868.42561         118   1.9802722           0
1986.jahrend   1159.8423   241.64825   4.7997132   4.699e-06   681.31297   1638.3716         118   1.9802722           0
1989.jahrend   1192.7782   208.74292   5.7141014   8.425e-08    779.4104    1606.146         118   1.9802722           0
1992.jahrend   773.37755   142.12717   5.4414476   2.910e-07   491.92706    1054.828         118   1.9802722           0
1993.jahrend    863.5443   111.03077   7.7775222   3.084e-12   643.67314   1083.4155         118   1.9802722           0
1994.jahrend   511.66505   150.36089   3.4029132   .00091116   213.90955   809.42054         118   1.9802722           0
1995.jahrend   828.36548   173.76006   4.7672952   5.378e-06   484.27326   1172.4577         118   1.9802722           0
1996.jahrend   1411.2514   322.63731   4.3741111   .00002645   772.34171   2050.1611         118   1.9802722           0
2000.jahrend   1401.6501   254.69986   5.5031441   2.204e-07   897.27499   1906.0251         118   1.9802722           0
2001.jahrend   1798.6421   418.89894   4.2937376   .00003624   969.10819   2628.1761         118   1.9802722           0
2002.jahrend   1282.6707   247.44879   5.1835804   9.113e-07   792.65471   1772.6866         118   1.9802722           0
2005.jahrend   1739.3916   4.0146526    433.2608   8.99e-191   1731.4415   1747.3417         118   1.9802722           0
2012.jahrend   327.85366   128.68411     2.54774   .01212745   73.024088   582.68323         118   1.9802722           0
       _cons  -98.272182   333.31246  -.29483501   .76863801  -758.32159   561.77722         118   1.9802722           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_ijahrend_13.xlsx saved

Linear regression                               Number of obs     =        136
                                                F(2, 133)         =       2.36
                                                Prob > F          =     0.0988
                                                R-squared         =     0.0804
                                                Root MSE          =     459.55

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .1342248   .0992715     1.35   0.179    -.0621304      .33058
          jahrend |   30.64286   15.69165     1.95   0.053    -.3946279    61.68034
            _cons |  -60078.45   31310.37    -1.92   0.057    -122009.2    1852.254
-----------------------------------------------------------------------------------

reg_jahrend_13[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .13422479    .0992715   1.3520979   .17863871  -.06213039   .33057998         133   1.9779613           0
     jahrend   30.642856   15.691654   1.9528124   .05294187  -.39462793    61.68034         133   1.9779613           0
       _cons  -60078.455   31310.375  -1.9188034    .0571517  -122009.16   1852.2538         133   1.9779613           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_jahrend_13.xlsx saved

Linear regression                               Number of obs     =        136
                                                F(3, 131)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.1940
                                                Root MSE          =     433.49

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .2663531   .0917483     2.90   0.004      .084853    .4478532
                  |
          decades |
           1990s  |  -102.8917   203.9999    -0.50   0.615    -506.4522    300.6687
           2000s  |   633.1287   254.8293     2.48   0.014     129.0156    1137.242
           2010s  |   -616.757   240.3565    -2.57   0.011     -1092.24   -141.2745
                  |
            _cons |   794.7926   277.9992     2.86   0.005     244.8439    1344.741
-----------------------------------------------------------------------------------

reg_idecades_13[6,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .26635311   .09174833    2.903084   .00433726   .08485303    .4478532         131   1.9782385           0
  1b.decades           0           .           .           .           .           .         131   1.9782385           0
   2.decades  -102.89174   203.99991  -.50437151   .61484762  -506.45222   300.66873         131   1.9782385           0
   3.decades   633.12868   254.82929   2.4845208   .01423364   129.01556   1137.2418         131   1.9782385           0
   4.decades    -616.757   240.35651  -2.5660091   .01141269  -1092.2395  -141.27449         131   1.9782385           0
       _cons   794.79264   277.99922   2.8589743   .00494672   244.84386   1344.7414         131   1.9782385           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_idecades_13.xlsx saved

Linear regression                               Number of obs     =        136
                                                F(2, 133)         =       1.91
                                                Prob > F          =     0.1528
                                                R-squared         =     0.0598
                                                Root MSE          =     464.66

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .1518937   .1015729     1.50   0.137    -.0490135    .3528009
          decades |   235.8257   141.5082     1.67   0.098    -44.07197    515.7234
            _cons |   485.3576    380.631     1.28   0.204    -267.5157    1238.231
-----------------------------------------------------------------------------------

reg_decades_13[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .15189367   .10157287   1.4954157   .13717502  -.04901353   .35280088         133   1.9779613           0
     decades   235.82574   141.50818   1.6665166   .09796393  -44.071965   515.72344         133   1.9779613           0
       _cons   485.35761   380.63095   1.2751396   .20448279  -267.51567   1238.2309         133   1.9779613           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_decades_13.xlsx saved
note: 2008.jahrend omitted because of collinearity.

Linear regression                               Number of obs     =         NA
                                                F(0, 0)           =          .
                                                Prob > F          =          .
                                                R-squared         =     1.0000
                                                Root MSE          =          0

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |  -.4959488          .        .       .            .           .
                  |
          jahrend |
            2008  |          0  (omitted)
                  |
            _cons |   3045.608          .        .       .            .           .
-----------------------------------------------------------------------------------

reg_ijahrend_14[4,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y  -.49594875           .           .           .           .           .           0           .           0
1980b.jahr~d           0           .           .           .           .           .           0           .           0
2008o.jahr~d           0           .           .           .           .           .           0           .           0
       _cons   3045.6084           .           .           .           .           .           0           .           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_ijahrend_14.xlsx saved
note: jahrend omitted because of collinearity.

Linear regression                               Number of obs     =         NA
                                                F(0, 0)           =          .
                                                Prob > F          =          .
                                                R-squared         =     1.0000
                                                Root MSE          =          0

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |  -.4959488          .        .       .            .           .
          jahrend |          0  (omitted)
            _cons |   3045.608          .        .       .            .           .
-----------------------------------------------------------------------------------

reg_jahrend_14[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y  -.49594875           .           .           .           .           .           0           .           0
   o.jahrend           0           .           .           .           .           .           0           .           0
       _cons   3045.6084           .           .           .           .           .           0           .           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_jahrend_14.xlsx saved
note: 3.decades omitted because of collinearity.

Linear regression                               Number of obs     =         NA
                                                F(0, 0)           =          .
                                                Prob > F          =          .
                                                R-squared         =     1.0000
                                                Root MSE          =          0

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |  -.4959488          .        .       .            .           .
                  |
          decades |
           2000s  |          0  (omitted)
            _cons |   3045.608          .        .       .            .           .
-----------------------------------------------------------------------------------

reg_idecades_14[4,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y  -.49594875           .           .           .           .           .           0           .           0
  1b.decades           0           .           .           .           .           .           0           .           0
  3o.decades           0           .           .           .           .           .           0           .           0
       _cons   3045.6084           .           .           .           .           .           0           .           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_idecades_14.xlsx saved
note: decades omitted because of collinearity.

Linear regression                               Number of obs     =         NA
                                                F(0, 0)           =          .
                                                Prob > F          =          .
                                                R-squared         =     1.0000
                                                Root MSE          =          0

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |  -.4959488          .        .       .            .           .
          decades |          0  (omitted)
            _cons |   3045.608          .        .       .            .           .
-----------------------------------------------------------------------------------

reg_decades_14[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y  -.49594875           .           .           .           .           .           0           .           0
   o.decades           0           .           .           .           .           .           0           .           0
       _cons   3045.6084           .           .           .           .           .           0           .           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_decades_14.xlsx saved

Linear regression                               Number of obs     =        322
                                                F(8, 309)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.1409
                                                Root MSE          =     330.42

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .1233888   .0487661     2.53   0.012     .0274332    .2193443
                  |
          jahrend |
            1986  |  -1439.222   15.32997   -93.88   0.000    -1469.386   -1409.057
            1989  |  -714.5293   469.2443    -1.52   0.129    -1637.848    208.7891
            1990  |  -149.3453   33.10007    -4.51   0.000    -214.4754   -84.21529
            1992  |  -992.4469    40.6682   -24.40   0.000    -1072.469   -912.4253
            1993  |  -1023.695   30.84466   -33.19   0.000    -1084.387   -963.0025
            1994  |  -1158.943   74.38659   -15.58   0.000    -1305.311   -1012.574
            1995  |  -1097.762   88.80837   -12.36   0.000    -1272.508   -923.0164
            1996  |  -1241.086   76.58617   -16.21   0.000    -1391.782    -1090.39
            1997  |  -1022.377   153.5135    -6.66   0.000    -1324.441    -720.313
            1998  |  -340.4739   16.47716   -20.66   0.000    -372.8955   -308.0522
            1999  |  -1701.652   88.13309   -19.31   0.000    -1875.069   -1528.235
                  |
            _cons |   2055.482   108.3892    18.96   0.000     1842.207    2268.756
-----------------------------------------------------------------------------------

reg_ijahrend_15[14,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .12338876   .04876608   2.5302171   .01189567   .02743317   .21934435         309   1.9676709           0
1980b.jahr~d           0           .           .           .           .           .         309   1.9676709           0
1986.jahrend  -1439.2218   15.329972  -93.882872   3.32e-229  -1469.3861  -1409.0575         309   1.9676709           0
1989.jahrend  -714.52933   469.24435  -1.5227234   .12885063  -1637.8478   208.78911         309   1.9676709           0
1990.jahrend  -149.34533   33.100069  -4.5119341   9.140e-06  -214.47537   -84.21529         309   1.9676709           0
1992.jahrend   -992.4469   40.668204   -24.40351   4.762e-74  -1072.4685  -912.42526         309   1.9676709           0
1993.jahrend  -1023.6947   30.844665  -33.188712   6.77e-104  -1084.3868  -963.00254         309   1.9676709           0
1994.jahrend  -1158.9426   74.386589  -15.579994   8.594e-41  -1305.3109  -1012.5743         309   1.9676709           0
1995.jahrend  -1097.7621   88.808373   -12.36102   8.614e-29  -1272.5077  -923.01643         309   1.9676709           0
1996.jahrend   -1241.086   76.586169  -16.205094   3.557e-43  -1391.7824  -1090.3897         309   1.9676709           0
1997.jahrend  -1022.3771   153.51354  -6.6598496   1.254e-10  -1324.4413  -720.31299         309   1.9676709           0
1998.jahrend  -340.47387   16.477159  -20.663384   3.484e-60   -372.8955  -308.05224         309   1.9676709           0
1999.jahrend  -1701.6523   88.133091  -19.307757   4.855e-55  -1875.0693  -1528.2354         309   1.9676709           0
       _cons   2055.4815   108.38921   18.963894   9.943e-54   1842.2072   2268.7558         309   1.9676709           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_ijahrend_15.xlsx saved

Linear regression                               Number of obs     =        322
                                                F(2, 319)         =       9.56
                                                Prob > F          =     0.0001
                                                R-squared         =     0.0742
                                                Root MSE          =     337.59

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .1669543   .0459067     3.64   0.000     .0766362    .2572723
          jahrend |  -55.90896   16.81664    -3.32   0.001    -88.99449   -22.82343
            _cons |   112362.6   33489.35     3.36   0.001     46474.73    178250.5
-----------------------------------------------------------------------------------

reg_jahrend_15[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .16695426   .04590666   3.6368196   .00032174   .07663618   .25727233         319   1.9674284           0
     jahrend   -55.90896   16.816637   -3.324622   .00098851  -88.994489  -22.823432         319   1.9674284           0
       _cons   112362.62   33489.346   3.3551752   .00088886   46474.735   178250.51         319   1.9674284           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_jahrend_15.xlsx saved

Linear regression                               Number of obs     =        322
                                                F(2, 319)         =       4.65
                                                Prob > F          =     0.0102
                                                R-squared         =     0.0459
                                                Root MSE          =      342.7

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .1022651    .042988     2.38   0.018     .0176893    .1868409
                  |
          decades |
           1990s  |  -436.8127   300.8895    -1.45   0.148    -1028.791    155.1659
            _cons |   1503.037   323.9836     4.64   0.000     865.6223    2140.451
-----------------------------------------------------------------------------------

reg_idecades_15[4,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .10226511     .042988   2.3789223   .01795141    .0176893   .18684092         319   1.9674284           0
  1b.decades           0           .           .           .           .           .         319   1.9674284           0
   2.decades  -436.81274   300.88954  -1.4517379   .14755726  -1028.7914   155.16589         319   1.9674284           0
       _cons   1503.0368   323.98359   4.6392374   5.111e-06   865.62229   2140.4513         319   1.9674284           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_idecades_15.xlsx saved

Linear regression                               Number of obs     =        322
                                                F(2, 319)         =       4.65
                                                Prob > F          =     0.0102
                                                R-squared         =     0.0459
                                                Root MSE          =      342.7

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .1022651    .042988     2.38   0.018     .0176893    .1868409
          decades |  -436.8127   300.8895    -1.45   0.148    -1028.791    155.1659
            _cons |    1939.85   620.0088     3.13   0.002     720.0267    3159.672
-----------------------------------------------------------------------------------

reg_decades_15[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .10226511     .042988   2.3789223   .01795141    .0176893   .18684092         319   1.9674284           0
     decades  -436.81274   300.88954  -1.4517379   .14755726  -1028.7914   155.16589         319   1.9674284           0
       _cons   1939.8495   620.00878   3.1287453   .00191759   720.02667   3159.6724         319   1.9674284           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_decades_15.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(2, 1)           =          .
                                                Prob > F          =          .
                                                R-squared         =     0.9471
                                                Root MSE          =     118.23

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .4283274   .1078767     3.97   0.157    -.9423764    1.799031
                  |
          jahrend |
            1982  |   147.7949   132.2289     1.12   0.465    -1532.333    1827.923
            1994  |    298.464   81.15087     3.68   0.169    -732.6555    1329.584
                  |
            _cons |   293.1824   195.4314     1.50   0.374    -2190.009    2776.374
-----------------------------------------------------------------------------------

reg_ijahrend_16[5,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .42832739   .10787672   3.9705265    .1570697  -.94237635   1.7990311          NA   12.706205           0
1981b.jahr~d           0           .           .           .           .           .          NA   12.706205           0
1982.jahrend   147.79492   132.22895   1.1177199   .46464794  -1532.3331    1827.923          NA   12.706205           0
1994.jahrend     298.464   81.150866   3.6778906   .16900805  -732.65552   1329.5835          NA   12.706205           0
       _cons   293.18245   195.43138   1.5001811   .37429862  -2190.0086   2776.3735          NA   12.706205           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_ijahrend_16.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(2, 2)           =     120.39
                                                Prob > F          =     0.0082
                                                R-squared         =     0.8841
                                                Root MSE          =     123.79

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .4350008   .1200264     3.62   0.068     -.081431    .9514326
          jahrend |   19.20429   2.244752     8.56   0.013     9.545902    28.86268
            _cons |  -37700.18   4359.853    -8.65   0.013    -56459.11   -18941.25
-----------------------------------------------------------------------------------

reg_jahrend_16[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .43500078   .12002637   3.6242101    .0684126  -.08143101   .95143258          NA   4.3026527           0
     jahrend    19.20429   2.2447519   8.5551948   .01338903   9.5459021   28.862678          NA   4.3026527           0
       _cons  -37700.182   4359.8528  -8.6471226   .01311141  -56459.114  -18941.249          NA   4.3026527           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_jahrend_16.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(1, 2)           =          .
                                                Prob > F          =          .
                                                R-squared         =     0.8646
                                                Root MSE          =      133.8

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .4321311   .1312956     3.29   0.081    -.1327881    .9970503
                  |
          decades |
           1990s  |   226.0763   33.01919     6.85   0.021     84.00623    368.1464
            _cons |   360.5863   157.4232     2.29   0.149    -316.7511    1037.924
-----------------------------------------------------------------------------------

reg_idecades_16[4,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y    .4321311   .13129557   3.2912847    .0812256  -.13278814   .99705034          NA   4.3026527           0
  1b.decades           0           .           .           .           .           .          NA   4.3026527           0
   2.decades   226.07633   33.019189   6.8468166    .0206724   84.006227   368.14643          NA   4.3026527           0
       _cons   360.58626   157.42319   2.2905537    .1491123  -316.75106   1037.9236          NA   4.3026527           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_idecades_16.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(1, 2)           =          .
                                                Prob > F          =          .
                                                R-squared         =     0.8646
                                                Root MSE          =      133.8

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .4321311   .1312956     3.29   0.081    -.1327881    .9970503
          decades |   226.0763   33.01919     6.85   0.021     84.00623    368.1464
            _cons |   134.5099   148.8281     0.90   0.461    -505.8456    774.8655
-----------------------------------------------------------------------------------

reg_decades_16[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y    .4321311   .13129557   3.2912847    .0812256  -.13278814   .99705034          NA   4.3026527           0
     decades   226.07633   33.019189   6.8468166    .0206724   84.006227   368.14643          NA   4.3026527           0
       _cons   134.50993   148.82808   .90379404   .46149703  -505.84562   774.86549          NA   4.3026527           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_decades_16.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(5, 82)          =          .
                                                Prob > F          =          .
                                                R-squared         =     0.1594
                                                Root MSE          =     401.75

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .6009743   .2917335     2.06   0.043     .0206235    1.181325
                  |
          jahrend |
            1993  |  -197.7711     173.32    -1.14   0.257    -542.5599    147.0176
            1994  |  -245.2803   205.1837    -1.20   0.235     -653.456    162.8955
            1996  |  -587.6844   228.1905    -2.58   0.012    -1041.628   -133.7408
            1997  |   102.5596   67.87614     1.51   0.135    -32.46767    237.5869
            1998  |  -565.5367   366.3576    -1.54   0.127    -1294.339    163.2653
            1999  |  -641.9237   324.8786    -1.98   0.052    -1288.211    4.363237
            2002  |   48.92057     192.79     0.25   0.800    -334.6001    432.4412
                  |
            _cons |   279.3166   446.1047     0.63   0.533    -608.1278    1166.761
-----------------------------------------------------------------------------------

reg_ijahrend_17[10,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .60097432   .29173349   2.0600114   .04256888   .02062347   1.1813252          NA   1.9893186           0
1992b.jahr~d           0           .           .           .           .           .          NA   1.9893186           0
1993.jahrend  -197.77114   173.32002   -1.141075   .25716028  -542.55988    147.0176          NA   1.9893186           0
1994.jahrend  -245.28028   205.18369   -1.195418   .23536948    -653.456   162.89545          NA   1.9893186           0
1996.jahrend  -587.68442   228.19053  -2.5754111   .01180707  -1041.6281  -133.74077          NA   1.9893186           0
1997.jahrend    102.5596    67.87614   1.5109816   .13463736  -32.467668   237.58686          NA   1.9893186           0
1998.jahrend  -565.53671   366.35762  -1.5436739     .126518  -1294.3387    163.2653          NA   1.9893186           0
1999.jahrend  -641.92374   324.87858  -1.9758882   .05153048  -1288.2107   4.3632368          NA   1.9893186           0
2002.jahrend   48.920566   192.78996   .25375059   .80032341  -334.60008   432.44121          NA   1.9893186           0
       _cons   279.31658   446.10469   .62612339   .53297305  -608.12775   1166.7609          NA   1.9893186           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_ijahrend_17.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(2, 88)          =       3.46
                                                Prob > F          =     0.0356
                                                R-squared         =     0.0958
                                                Root MSE          =     402.22

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .4148175   .1938554     2.14   0.035     .0295707    .8000644
          jahrend |  -38.73552   37.96514    -1.02   0.310    -114.1832    36.71221
            _cons |   77710.65   75368.89     1.03   0.305    -72069.17    227490.5
-----------------------------------------------------------------------------------

reg_jahrend_17[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .41481754   .19385539   2.1398298   .03513626   .02957068    .8000644          NA   1.9872899           0
     jahrend  -38.735519   37.965136  -1.0202919   .31038686  -114.18325   36.712211          NA   1.9872899           0
       _cons   77710.653   75368.888   1.0310707   .30533315  -72069.173   227490.48          NA   1.9872899           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_jahrend_17.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(1, 88)          =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0789
                                                Root MSE          =     405.96

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .3090455   .1284751     2.41   0.018     .0537283    .5643628
                  |
          decades |
           2000s  |   244.1975    62.4413     3.91   0.000     120.1085    368.2864
            _cons |    709.396   228.0138     3.11   0.003     256.2665    1162.526
-----------------------------------------------------------------------------------

reg_idecades_17[4,9]
                      b         se          t     pvalue         ll         ul         df       crit      eform
wcoal_end_~y  .30904555  .12847512  2.4054895  .01824462  .05372825  .56436284         NA  1.9872899          0
  2b.decades          0          .          .          .          .          .         NA  1.9872899          0
   3.decades  244.19747  62.441299  3.9108327  .00018058  120.10851  368.28643         NA  1.9872899          0
       _cons  709.39601  228.01382  3.1111974  .00251218  256.26646  1162.5256         NA  1.9872899          0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_idecades_17.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(1, 88)          =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0789
                                                Root MSE          =     405.96

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .3090455   .1284751     2.41   0.018     .0537283    .5643628
          decades |   244.1975    62.4413     3.91   0.000     120.1085    368.2864
            _cons |   221.0011   166.9716     1.32   0.189    -110.8198     552.822
-----------------------------------------------------------------------------------

reg_decades_17[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .30904555   .12847512   2.4054895   .01824462   .05372825   .56436284          NA   1.9872899           0
     decades   244.19747   62.441299   3.9108327   .00018058   120.10851   368.28643          NA   1.9872899           0
       _cons   221.00107   166.97158    1.323585   .18906921  -110.81985   552.82199          NA   1.9872899           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_decades_17.xlsx saved
insufficient observations
insufficient observations
insufficient observations
insufficient observations

Linear regression                               Number of obs     =      1,586
                                                F(20, 1557)       =          .
                                                Prob > F          =          .
                                                R-squared         =     0.1198
                                                Root MSE          =     390.15

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .1928717   .0336847     5.73   0.000     .1267996    .2589438
                  |
          jahrend |
            1979  |   131.6458   13.66952     9.63   0.000     104.8331    158.4584
            1980  |   920.2533    21.4982    42.81   0.000     878.0848    962.4218
            1981  |   474.6909    28.2469    16.81   0.000     419.2849    530.0969
            1982  |  -60.87845   24.13572    -2.52   0.012    -108.2204   -13.53651
            1983  |   904.6555   350.3555     2.58   0.010     217.4371    1591.874
            1984  |   269.1371   13.72385    19.61   0.000     242.2179    296.0562
            1985  |   1100.568   250.0659     4.40   0.000     610.0664    1591.069
            1986  |   897.4282   176.6202     5.08   0.000     550.9896    1243.867
            1987  |   960.6541   202.5232     4.74   0.000     563.4071    1357.901
            1988  |     1691.6   469.1801     3.61   0.000     771.3083    2611.891
            1989  |   1061.767   187.4796     5.66   0.000     694.0277    1429.506
            1990  |   756.5944   142.8338     5.30   0.000     476.4275    1036.761
            1991  |   879.3192   142.3786     6.18   0.000     600.0452    1158.593
            1992  |   471.4775   13.86363    34.01   0.000     444.2842    498.6709
            1993  |   414.1224    22.0278    18.80   0.000     370.9151    457.3297
            1994  |   363.2999   39.35993     9.23   0.000     286.0958    440.5039
            1995  |    487.957   41.92737    11.64   0.000     405.7169    570.1971
            1996  |   437.4489   58.75199     7.45   0.000     322.2075    552.6903
            1997  |   314.9415   151.0551     2.08   0.037     18.64861    611.2344
            1998  |   930.3892   269.4422     3.45   0.001     401.8813    1458.897
            1999  |   638.7074   194.4802     3.28   0.001     257.2366    1020.178
            2000  |   612.2263   154.7183     3.96   0.000     308.7482    915.7045
            2001  |   1115.462   287.7788     3.88   0.000     550.9871    1679.937
            2003  |   1200.678   21.53284    55.76   0.000     1158.442    1242.915
            2004  |   -55.4721   8.835251    -6.28   0.000    -72.80235   -38.14186
            2005  |  -147.0985   59.28471    -2.48   0.013    -263.3848   -30.81222
            2006  |   384.5412   235.2066     1.63   0.102    -76.81388    845.8963
                  |
            _cons |   522.4463   55.78374     9.37   0.000     413.0271    631.8655
-----------------------------------------------------------------------------------

reg_ijahrend_19[30,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .19287169   .03368468   5.7257985   1.233e-08   .12679957   .25894381        1557   1.9614888           0
1978b.jahr~d           0           .           .           .           .           .        1557   1.9614888           0
1979.jahrend   131.64575    13.66952   9.6306051   2.308e-21   104.83314   158.45836        1557   1.9614888           0
1980.jahrend    920.2533   21.498199   42.806066   2.75e-265   878.08483   962.42178        1557   1.9614888           0
1981.jahrend   474.69088   28.246899   16.805062   2.267e-58   419.28491   530.09686        1557   1.9614888           0
1982.jahrend  -60.878453   24.135719  -2.5223385   .01175674  -108.22039  -13.536512        1557   1.9614888           0
1983.jahrend   904.65553   350.35552   2.5821073   .00991053   217.43712   1591.8739        1557   1.9614888           0
1984.jahrend   269.13706   13.723847   19.610905   1.054e-76   242.21789   296.05623        1557   1.9614888           0
1985.jahrend   1100.5678   250.06588   4.4011114    .0000115   610.06637   1591.0692        1557   1.9614888           0
1986.jahrend   897.42823   176.62025   5.0811175   4.205e-07    550.9896   1243.8669        1557   1.9614888           0
1987.jahrend   960.65409   202.52321   4.7434272   2.294e-06   563.40709   1357.9011        1557   1.9614888           0
1988.jahrend   1691.5997   469.18007   3.6054382   .00032145   771.30829   2611.8911        1557   1.9614888           0
1989.jahrend   1061.7667   187.47956   5.6633732   1.765e-08   694.02767   1429.5058        1557   1.9614888           0
1990.jahrend   756.59441   142.83381   5.2970259   1.345e-07   476.42749   1036.7613        1557   1.9614888           0
1991.jahrend   879.31917   142.37856   6.1759239   8.377e-10   600.04522   1158.5931        1557   1.9614888           0
1992.jahrend   471.47753   13.863626   34.008241   4.75e-190   444.28418   498.67088        1557   1.9614888           0
1993.jahrend   414.12243   22.027803   18.799987   3.201e-71   370.91514   457.32972        1557   1.9614888           0
1994.jahrend   363.29987   39.359926    9.230197   8.541e-20   286.09581   440.50392        1557   1.9614888           0
1995.jahrend   487.95699   41.927367    11.63815   4.454e-30   405.71693   570.19705        1557   1.9614888           0
1996.jahrend   437.44889   58.751988   7.4456865   1.589e-13   322.20752   552.69025        1557   1.9614888           0
1997.jahrend    314.9415    151.0551   2.0849445   .03723692   18.648612   611.23439        1557   1.9614888           0
1998.jahrend   930.38918    269.4422   3.4530195    .0005692   401.88133    1458.897        1557   1.9614888           0
1999.jahrend   638.70736   194.48019   3.2841769   .00104551   257.23665   1020.1781        1557   1.9614888           0
2000.jahrend   612.22634   154.71825   3.9570402    .0000793   308.74823   915.70446        1557   1.9614888           0
2001.jahrend   1115.4619   287.77878   3.8761089   .00011055   550.98706   1679.9368        1557   1.9614888           0
2003.jahrend   1200.6783   21.532837   55.760337           0   1158.4418   1242.9147        1557   1.9614888           0
2004.jahrend  -55.472105   8.8352507  -6.2784981   4.424e-10   -72.80235   -38.14186        1557   1.9614888           0
2005.jahrend  -147.09852   59.284715  -2.4812218   .01319818  -263.38483  -30.812223        1557   1.9614888           0
2006.jahrend   384.54123   235.20661   1.6349083   .10227041  -76.813885   845.89635        1557   1.9614888           0
       _cons   522.44631   55.783743   9.3655658   2.557e-20   413.02713    631.8655        1557   1.9614888           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_ijahrend_19.xlsx saved

Linear regression                               Number of obs     =      1,586
                                                F(2, 1583)        =      20.65
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0583
                                                Root MSE          =     400.23

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |    .192549   .0299871     6.42   0.000     .1337304    .2513676
          jahrend |  -16.95808   7.609906    -2.23   0.026    -31.88463   -2.031525
            _cons |   34772.41   15147.64     2.30   0.022     5060.858    64483.96
-----------------------------------------------------------------------------------

reg_jahrend_19[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .19254897   .02998711   6.4210584   1.785e-10   .13373035    .2513676        1583   1.9614637           0
     jahrend  -16.958079   7.6099058  -2.2284217   .02599239  -31.884633  -2.0315252        1583   1.9614637           0
       _cons   34772.409   15147.642   2.2955657   .02183093   5060.8582   64483.959        1583   1.9614637           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_jahrend_19.xlsx saved

Linear regression                               Number of obs     =      1,586
                                                F(3, 1582)        =      19.02
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0792
                                                Root MSE          =      395.9

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .1687754   .0278457     6.06   0.000     .1141571    .2233937
                  |
          decades |
           1990s  |  -467.1848   108.4968    -4.31   0.000    -679.9975   -254.3721
           2000s  |  -403.9399   174.6762    -2.31   0.021     -746.561   -61.31877
                  |
            _cons |   1482.333   121.3646    12.21   0.000     1244.281    1720.386
-----------------------------------------------------------------------------------

reg_idecades_19[5,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .16877539   .02784566   6.0611016   1.687e-09   .11415711   .22339367        1582   1.9614647           0
  1b.decades           0           .           .           .           .           .        1582   1.9614647           0
   2.decades  -467.18479   108.49684   -4.305976   .00001765   -679.9975  -254.37207        1582   1.9614647           0
   3.decades  -403.93989   174.67616  -2.3125072   .02087755  -746.56101  -61.318766        1582   1.9614647           0
       _cons   1482.3332   121.36463   12.213882   7.509e-33   1244.2808   1720.3857        1582   1.9614647           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_idecades_19.xlsx saved

Linear regression                               Number of obs     =      1,586
                                                F(2, 1583)        =      24.15
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0686
                                                Root MSE          =     398.03

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |     .17301   .0282306     6.13   0.000     .1176367    .2283833
          decades |  -300.5436   91.92545    -3.27   0.001     -480.852   -120.2352
            _cons |   1614.873   190.9916     8.46   0.000      1240.25    1989.496
-----------------------------------------------------------------------------------

reg_decades_19[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .17300998   .02823061   6.1284529   1.118e-09   .11763666    .2283833        1583   1.9614637           0
     decades  -300.54361   91.925449  -3.2694278   .00110079  -480.85205  -120.23518        1583   1.9614637           0
       _cons   1614.8734   190.99157   8.4552077   6.235e-17   1240.2504   1989.4965        1583   1.9614637           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_decades_19.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(2, 1)           =          .
                                                Prob > F          =          .
                                                R-squared         =     0.8711
                                                Root MSE          =     746.76

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |  -1.796204   3.709476    -0.48   0.713    -48.92957    45.33716
                  |
          jahrend |
            1992  |   1625.874    5131.26     0.32   0.805    -63572.97    66824.72
            2010  |   2406.883   5010.694     0.48   0.715    -61260.02    66073.78
            2012  |   3788.826   4717.666     0.80   0.569    -56154.81    63732.46
            2013  |   1909.282   5588.054     0.34   0.790    -69093.68    72912.24
                  |
            _cons |    5550.28   7693.699     0.72   0.602    -92207.43      103308
-----------------------------------------------------------------------------------

reg_ijahrend_20[7,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y  -1.7962042    3.709476  -.48422047   .71291982  -48.929565   45.337157          NA   12.706205           0
1982b.jahr~d           0           .           .           .           .           .          NA   12.706205           0
1992.jahrend   1625.8737   5131.2602   .31685661    .8046539  -63572.969   66824.716          NA   12.706205           0
2010.jahrend   2406.8831   5010.6936   .48034929   .71491924  -61260.015   66073.781          NA   12.706205           0
2012.jahrend   3788.8258   4717.6662   .80311444   .56923944  -56154.807   63732.458          NA   12.706205           0
2013.jahrend    1909.282    5588.054   .34167207   .79040151  -69093.677   72912.241          NA   12.706205           0
       _cons     5550.28   7693.6987   .72140595   .60214551  -92207.431   103307.99          NA   12.706205           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_ijahrend_20.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(2, 4)           =       3.44
                                                Prob > F          =     0.1351
                                                R-squared         =     0.3499
                                                Root MSE          =     838.63

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   -1.34211   .5260042    -2.55   0.063    -2.802532    .1183119
          jahrend |   66.40314   36.29555     1.83   0.141    -34.36945    167.1757
            _cons |  -126912.9   71002.96    -1.79   0.148    -324048.7    70222.95
-----------------------------------------------------------------------------------

reg_jahrend_20[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y  -1.3421099   .52600419  -2.5515193    .0632035  -2.8025316    .1183119          NA   2.7764451           0
     jahrend   66.403145   36.295548   1.8295121   .14130756  -34.369452   167.17574          NA   2.7764451           0
       _cons  -126912.88   71002.962  -1.7874308   .14839399   -324048.7   70222.948          NA   2.7764451           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_jahrend_20.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(2, 3)           =          .
                                                Prob > F          =          .
                                                R-squared         =     0.5385
                                                Root MSE          =     815.91

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |  -4.481021   2.997869    -1.49   0.232    -14.02158    5.059537
                  |
          decades |
           1990s  |   5327.613   4179.117     1.27   0.292    -7972.204    18627.43
           2010s  |   6264.347   4256.169     1.47   0.237     -7280.68    19809.38
                  |
            _cons |   11110.69   6239.309     1.78   0.173    -8745.577    30966.95
-----------------------------------------------------------------------------------

reg_idecades_20[5,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y  -4.4810209   2.9978691  -1.4947353   .23185138  -14.021578   5.0595367          NA   3.1824463           0
  1b.decades           0           .           .           .           .           .          NA   3.1824463           0
   2.decades   5327.6125   4179.1174   1.2748176   .29214835  -7972.2041   18627.429          NA   3.1824463           0
   4.decades   6264.3474   4256.1685   1.4718279   .23745608  -7280.6804   19809.375          NA   3.1824463           0
       _cons   11110.687   6239.3087   1.7807562   .17298625  -8745.5773   30966.952          NA   3.1824463           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_idecades_20.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(2, 4)           =       3.66
                                                Prob > F          =     0.1249
                                                R-squared         =     0.3536
                                                Root MSE          =     836.25

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |  -1.320476    .493251    -2.68   0.055     -2.68996    .0490083
          decades |   640.6274   324.2712     1.98   0.119    -259.6938    1540.949
            _cons |   4006.725   899.3584     4.46   0.011     1509.705    6503.744
-----------------------------------------------------------------------------------

reg_decades_20[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   -1.320476   .49325102  -2.6770873   .05539605  -2.6899604   .04900833          NA   2.7764451           0
     decades   640.62738   324.27121   1.9755913   .11940256  -259.69382   1540.9486          NA   2.7764451           0
       _cons   4006.7246   899.35836   4.4550924   .01120261   1509.7055   6503.7437          NA   2.7764451           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_decades_20.xlsx saved

Linear regression                               Number of obs     =      2,790
                                                F(20, 2761)       =          .
                                                Prob > F          =          .
                                                R-squared         =     0.1081
                                                Root MSE          =      399.6

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .2518172   .0231583    10.87   0.000     .2064079    .2972266
                  |
          jahrend |
            1983  |    286.231   300.7674     0.95   0.341    -303.5208    875.9828
            1984  |  -858.8418   169.5688    -5.06   0.000    -1191.336   -526.3473
            1985  |   455.2457   313.1315     1.45   0.146    -158.7499    1069.241
            1986  |  -460.5995   213.4467    -2.16   0.031    -879.1309   -42.06806
            1987  |  -336.1233   16.98502   -19.79   0.000    -369.4279   -302.8187
            1988  |   188.5761   272.0432     0.69   0.488    -344.8526    722.0048
            1989  |   791.8377   16.17776    48.95   0.000      760.116    823.5595
            1990  |  -829.4469   33.96435   -24.42   0.000     -896.045   -762.8488
            1991  |  -480.1692   428.9507    -1.12   0.263    -1321.266    360.9275
            1992  |  -345.0453   20.65872   -16.70   0.000    -385.5534   -304.5372
            1993  |  -400.7739   13.93051   -28.77   0.000    -428.0892   -373.4586
            1994  |  -451.8978   30.25238   -14.94   0.000    -511.2174   -392.5782
            1995  |  -368.1476   27.06338   -13.60   0.000    -421.2141    -315.081
            1996  |  -359.9213   39.88776    -9.02   0.000    -438.1341   -281.7084
            1997  |   -295.365    110.698    -2.67   0.008    -512.4242   -78.30568
            1998  |  -549.3794   124.3855    -4.42   0.000    -793.2773   -305.4814
            1999  |  -228.6123   147.3229    -1.55   0.121    -517.4865    60.26189
            2000  |  -464.1249   177.6331    -2.61   0.009    -812.4321   -115.8178
            2001  |   -578.084   153.9801    -3.75   0.000    -880.0117   -276.1562
            2002  |  -84.04709   456.9977    -0.18   0.854    -980.1389    812.0447
            2003  |  -942.7078   138.6299    -6.80   0.000    -1214.536   -670.8791
            2004  |   110.0097   373.0053     0.29   0.768    -621.3879    841.4074
            2005  |   13.26893    330.139     0.04   0.968    -634.0755    660.6134
            2006  |  -1.812646   31.86925    -0.06   0.955    -64.30263    60.67734
            2007  |  -899.2267   23.26432   -38.65   0.000     -944.844   -853.6095
            2008  |   -1117.94   12.14442   -92.05   0.000    -1141.753   -1094.127
            2010  |  -359.4104   17.38371   -20.68   0.000    -393.4968    -325.324
                  |
            _cons |   1225.222    57.9733    21.13   0.000     1111.547    1338.898
-----------------------------------------------------------------------------------

reg_ijahrend_21[30,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .25181725   .02315829   10.873739   5.376e-27   .20640792   .29722657        2761   1.9608236           0
1981b.jahr~d           0           .           .           .           .           .        2761   1.9608236           0
1983.jahrend   286.23101   300.76739   .95166901   .34134817  -303.52078   875.98279        2761   1.9608236           0
1984.jahrend  -858.84179   169.56878  -5.0648579   4.356e-07  -1191.3363  -526.34732        2761   1.9608236           0
1985.jahrend   455.24565   313.13148   1.4538482   .14610204  -158.74993   1069.2412        2761   1.9608236           0
1986.jahrend  -460.59945   213.44674  -2.1579128   .03102059  -879.13085  -42.068058        2761   1.9608236           0
1987.jahrend  -336.12329    16.98502  -19.789396   1.286e-81  -369.42792  -302.81866        2761   1.9608236           0
1988.jahrend   188.57612    272.0432   .69318449   .48825209  -344.85259   722.00484        2761   1.9608236           0
1989.jahrend   791.83772   16.177765   48.946052           0   760.11597   823.55946        2761   1.9608236           0
1990.jahrend  -829.44694   33.964353  -24.421103   2.01e-119  -896.04504  -762.84884        2761   1.9608236           0
1991.jahrend  -480.16918   428.95071  -1.1194041   .26306515  -1321.2658   360.92747        2761   1.9608236           0
1992.jahrend  -345.04528   20.658725  -16.702158   9.798e-60   -385.5534  -304.53717        2761   1.9608236           0
1993.jahrend  -400.77389   13.930506  -28.769514   2.01e-159  -428.08916  -373.45863        2761   1.9608236           0
1994.jahrend  -451.89779   30.252381  -14.937594   1.408e-48  -511.21737  -392.57821        2761   1.9608236           0
1995.jahrend  -368.14756   27.063378  -13.603163   7.710e-41  -421.21407  -315.08105        2761   1.9608236           0
1996.jahrend  -359.92129   39.887758  -9.0233522   3.334e-19  -438.13414  -281.70843        2761   1.9608236           0
1997.jahrend  -295.36496   110.69802  -2.6682046   .00767036  -512.42424  -78.305682        2761   1.9608236           0
1998.jahrend  -549.37936   124.38546   -4.416749   .00001041   -793.2773  -305.48142        2761   1.9608236           0
1999.jahrend  -228.61232    147.3229  -1.5517772   .12083014  -517.48653   60.261889        2761   1.9608236           0
2000.jahrend  -464.12494    177.6331  -2.6128291   .00902832  -812.43211  -115.81777        2761   1.9608236           0
2001.jahrend  -578.08397   153.98008  -3.7542777   .00017743  -880.01173   -276.1562        2761   1.9608236           0
2002.jahrend  -84.047092   456.99766  -.18391143   .85409644  -980.13887   812.04468        2761   1.9608236           0
2003.jahrend  -942.70781   138.62985  -6.8001788   1.276e-11  -1214.5365  -670.87912        2761   1.9608236           0
2004.jahrend   110.00974   373.00533   .29492808     .768071   -621.3879   841.40738        2761   1.9608236           0
2005.jahrend   13.268929   330.13905   .04019194   .96794301   -634.0755   660.61335        2761   1.9608236           0
2006.jahrend  -1.8126462   31.869253  -.05687759   .95464682  -64.302629   60.677337        2761   1.9608236           0
2007.jahrend  -899.22675   23.264322   -38.65261   1.26e-261  -944.84398  -853.60952        2761   1.9608236           0
2008.jahrend  -1117.9403   12.144422  -92.053805           0  -1141.7533  -1094.1272        2761   1.9608236           0
2010.jahrend   -359.4104   17.383706   -20.67513   2.066e-88  -393.49678  -325.32401        2761   1.9608236           0
       _cons   1225.2223   57.973296   21.134253   5.109e-92   1111.5469   1338.8977        2761   1.9608236           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_ijahrend_21.xlsx saved

Linear regression                               Number of obs     =      2,790
                                                F(2, 2787)        =      65.38
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0796
                                                Root MSE          =     404.03

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .2336029   .0204749    11.41   0.000     .1934554    .2737504
          jahrend |  -8.874425   6.069624    -1.46   0.144    -20.77584    3.026987
            _cons |   18576.61   12094.19     1.54   0.125    -5137.859    42291.08
-----------------------------------------------------------------------------------

reg_jahrend_21[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .23360292   .02047491    11.40923   1.680e-29   .19345541   .27375044        2787   1.9608155           0
     jahrend  -8.8744252   6.0696238  -1.4621047   .14382528  -20.775838   3.0269875        2787   1.9608155           0
       _cons   18576.612   12094.188    1.535995   .12465311  -5137.8587   42291.083        2787   1.9608155           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_jahrend_21.xlsx saved

Linear regression                               Number of obs     =      2,790
                                                F(3, 2785)        =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0804
                                                Root MSE          =     404.01

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .2236035   .0202613    11.04   0.000     .1838748    .2633322
                  |
          decades |
           1990s  |  -222.9233   153.3709    -1.45   0.146    -523.6555    77.80883
           2000s  |  -273.5655   196.0111    -1.40   0.163    -657.9071    110.7762
           2010s  |  -216.3304   153.5291    -1.41   0.159    -517.3727    84.71199
                  |
            _cons |   1131.593   160.8552     7.03   0.000     816.1851        1447
-----------------------------------------------------------------------------------

reg_idecades_21[6,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .22360352    .0202613   11.035988   9.546e-28   .18387483   .26333222        2785   1.9608162           0
  1b.decades           0           .           .           .           .           .        2785   1.9608162           0
   2.decades  -222.92334   153.37092  -1.4534916   .14619998  -523.65552   77.808829        2785   1.9608162           0
   3.decades  -273.56546   196.01108  -1.3956633   .16292717  -657.90715   110.77622        2785   1.9608162           0
   4.decades  -216.33038   153.52911  -1.4090512   .15893168  -517.37274   84.711992        2785   1.9608162           0
       _cons   1131.5925   160.85518   7.0348526   2.501e-12   816.18507        1447        2785   1.9608162           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_idecades_21.xlsx saved

Linear regression                               Number of obs     =      2,790
                                                F(2, 2787)        =      63.80
                                                Prob > F          =     0.0000
                                                R-squared         =     0.0795
                                                Root MSE          =     404.06

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .2267026   .0206748    10.97   0.000      .186163    .2672421
          decades |  -103.0263   90.34806    -1.14   0.254    -280.1822    74.12956
            _cons |   1109.922   191.3518     5.80   0.000     734.7165    1485.128
-----------------------------------------------------------------------------------

reg_decades_21[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .22670256   .02067484    10.96514   2.025e-27     .186163   .26724212        2787   1.9608155           0
     decades  -103.02631   90.348055  -1.1403268   .25424815  -280.18218    74.12956        2787   1.9608155           0
       _cons   1109.9222   191.35183   5.8004263   7.359e-09   734.71653   1485.1278        2787   1.9608155           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_decades_21.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(1, 2)           =          .
                                                Prob > F          =          .
                                                R-squared         =     0.9971
                                                Root MSE          =     86.777

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .6086472   .0394856    15.41   0.004     .4387545    .7785398
                  |
          jahrend |
            1987  |   344.1546   41.94085     8.21   0.015     163.6977    524.6115
            2003  |  -472.1738   31.07512   -15.19   0.004    -605.8793   -338.4684
            2007  |  -378.2378   97.76767    -3.87   0.061    -798.8982    42.42254
                  |
            _cons |   404.0225   68.33721     5.91   0.027     109.9913    698.0538
-----------------------------------------------------------------------------------

reg_ijahrend_22[6,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .60864716   .03948556   15.414425   .00418229   .43875452    .7785398          NA   4.3026527           0
1982b.jahr~d           0           .           .           .           .           .          NA   4.3026527           0
1987.jahrend   344.15457   41.940849   8.2057129   .01452853   163.69766   524.61148          NA   4.3026527           0
2003.jahrend  -472.17385   31.075118  -15.194595    .0043034  -605.87929  -338.46841          NA   4.3026527           0
2007.jahrend  -378.23781   97.767675   -3.868741   .06078479  -798.89816   42.422543          NA   4.3026527           0
       _cons   404.02253   68.337205   5.9121899   .02743707   109.99127    698.0538          NA   4.3026527           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_ijahrend_22.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(2, 4)           =     136.01
                                                Prob > F          =     0.0002
                                                R-squared         =     0.9639
                                                Root MSE          =     218.18

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .6198704   .0385868    16.06   0.000     .5127364    .7270044
          jahrend |  -19.30655   4.049712    -4.77   0.009    -30.55035   -8.062744
            _cons |    38706.4   8037.929     4.82   0.009     16389.53    61023.27
-----------------------------------------------------------------------------------

reg_jahrend_22[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .61987038   .03858675   16.064332   .00008781   .51273639   .72700438          NA   2.7764451           0
     jahrend  -19.306547    4.049712  -4.7673878   .00885703   -30.55035  -8.0627442          NA   2.7764451           0
       _cons   38706.404   8037.9291   4.8154697   .00855117   16389.535   61023.272          NA   2.7764451           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_jahrend_22.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(2, 4)           =     194.19
                                                Prob > F          =     0.0001
                                                R-squared         =     0.9784
                                                Root MSE          =     168.96

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .6103738   .0337491    18.09   0.000     .5166712    .7040765
                  |
          decades |
           2000s  |   -493.448   98.22705    -5.02   0.007      -766.17    -220.726
            _cons |   467.8337   134.0927     3.49   0.025     95.53256    840.1348
-----------------------------------------------------------------------------------

reg_idecades_22[4,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .61037381   .03374915   18.085606   .00005496   .51667116   .70407647          NA   2.7764451           0
  1b.decades           0           .           .           .           .           .          NA   2.7764451           0
   3.decades  -493.44797   98.227049  -5.0235447   .00736692  -766.16998  -220.72597          NA   2.7764451           0
       _cons   467.83368   134.09274   3.4888816   .02514835   95.532558    840.1348          NA   2.7764451           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_idecades_22.xlsx saved

Linear regression                               Number of obs     =         NA
                                                F(2, 4)           =     194.19
                                                Prob > F          =     0.0001
                                                R-squared         =     0.9784
                                                Root MSE          =     168.96

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .6103738   .0337491    18.09   0.000     .5166712    .7040765
          decades |   -246.724   49.11352    -5.02   0.007     -383.085    -110.363
            _cons |   714.5577   172.0327     4.15   0.014     236.9184    1192.197
-----------------------------------------------------------------------------------

reg_decades_22[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .61037381   .03374915   18.085606   .00005496   .51667116   .70407647          NA   2.7764451           0
     decades  -246.72399   49.113525  -5.0235447   .00736692  -383.08499  -110.36298          NA   2.7764451           0
       _cons   714.55767   172.03266   4.1536164   .01421853   236.91844   1192.1969          NA   2.7764451           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_decades_22.xlsx saved

Linear regression                               Number of obs     =        425
                                                F(15, 408)        =          .
                                                Prob > F          =          .
                                                R-squared         =     0.1265
                                                Root MSE          =      382.7

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .2172464   .0558099     3.89   0.000     .1075355    .3269573
                  |
          jahrend |
            1992  |  -962.0779    76.5685   -12.56   0.000    -1112.596   -811.5599
            1993  |  -1021.899   64.71982   -15.79   0.000    -1149.125    -894.673
            1994  |  -1112.718   84.48566   -13.17   0.000      -1278.8   -946.6368
            1995  |  -1065.492   95.70473   -11.13   0.000    -1253.628   -877.3563
            1996  |  -1170.252   108.6084   -10.77   0.000    -1383.753   -956.7496
            1997  |  -1087.678   132.0709    -8.24   0.000    -1347.303    -828.054
            1998  |  -296.0921    653.204    -0.45   0.651    -1580.158    987.9732
            1999  |  -1266.806    279.985    -4.52   0.000    -1817.199   -716.4125
            2000  |  -1201.889   377.3023    -3.19   0.002    -1943.588   -460.1901
            2001  |  -852.3598   181.4676    -4.70   0.000    -1209.088   -495.6316
            2002  |  -1302.959   231.1957    -5.64   0.000    -1757.443   -848.4756
            2003  |  -706.1879   185.6959    -3.80   0.000    -1071.228   -341.1478
            2004  |  -688.1333   30.75629   -22.37   0.000    -748.5939   -627.6727
            2005  |  -963.2433   231.3865    -4.16   0.000    -1418.102   -508.3847
            2013  |  -1220.254   2.546941  -479.11   0.000    -1225.261   -1215.247
                  |
            _cons |   1931.031   170.2722    11.34   0.000      1596.31    2265.751
-----------------------------------------------------------------------------------

reg_ijahrend_23[18,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .21724641   .05580993    3.892612   .00011581    .1075355   .32695731         408   1.9657954           0
1989b.jahr~d           0           .           .           .           .           .         408   1.9657954           0
1992.jahrend   -962.0779   76.568503   -12.56493   7.765e-31  -1112.5959  -811.55989         408   1.9657954           0
1993.jahrend  -1021.8989   64.719817  -15.789583   3.586e-44  -1149.1248    -894.673         408   1.9657954           0
1994.jahrend  -1112.7183   84.485665  -13.170498   2.943e-33  -1278.7998  -946.63679         408   1.9657954           0
1995.jahrend  -1065.4922   95.704726   -11.13312   2.552e-25  -1253.6281  -877.35632         408   1.9657954           0
1996.jahrend  -1170.2515   108.60842  -10.774961   5.423e-24  -1383.7535   -956.7496         408   1.9657954           0
1997.jahrend  -1087.6783   132.07085  -8.2355666   2.448e-15  -1347.3026  -828.05403         408   1.9657954           0
1998.jahrend  -296.09215   653.20398  -.45329201   .65057946  -1580.1575   987.97321         408   1.9657954           0
1999.jahrend  -1266.8057   279.98502  -4.5245482   7.949e-06   -1817.199  -716.41248         408   1.9657954           0
2000.jahrend  -1201.8892   377.30231  -3.1854807   .00155616  -1943.5884   -460.1901         408   1.9657954           0
2001.jahrend  -852.35983   181.46762  -4.6970354   3.613e-06   -1209.088  -495.63162         408   1.9657954           0
2002.jahrend  -1302.9591   231.19575  -5.6357399   3.256e-08  -1757.4426  -848.47557         408   1.9657954           0
2003.jahrend  -706.18792   185.69591  -3.8029267   .00016485  -1071.2281  -341.14777         408   1.9657954           0
2004.jahrend  -688.13328   30.756291  -22.373741   6.210e-73  -748.59385  -627.67271         408   1.9657954           0
2005.jahrend  -963.24332   231.38653  -4.1629186   .00003835  -1418.1019  -508.38474         408   1.9657954           0
2013.jahrend  -1220.2539   2.5469413  -479.10561           0  -1225.2606  -1215.2471         408   1.9657954           0
       _cons   1931.0306    170.2722   11.340845   4.234e-26   1596.3103   2265.7509         408   1.9657954           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_ijahrend_23.xlsx saved

Linear regression                               Number of obs     =        425
                                                F(2, 422)         =       7.31
                                                Prob > F          =     0.0008
                                                R-squared         =     0.0656
                                                Root MSE          =     389.19

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .2165058   .0567991     3.81   0.000     .1048614    .3281502
          jahrend |  -9.020083   8.849513    -1.02   0.309     -26.4147    8.374532
            _cons |   18911.04   17601.73     1.07   0.283    -15686.94    53509.02
-----------------------------------------------------------------------------------

reg_jahrend_23[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .21650582   .05679912   3.8117815   .00015851    .1048614   .32815024         422   1.9656014           0
     jahrend  -9.0200834   8.8495133  -1.0192745   .30865664  -26.414699   8.3745319         422   1.9656014           0
       _cons   18911.043   17601.727   1.0743857   .28326379  -15686.935   53509.022         422   1.9656014           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_jahrend_23.xlsx saved

Linear regression                               Number of obs     =        425
                                                F(2, 420)         =          .
                                                Prob > F          =          .
                                                R-squared         =     0.0789
                                                Root MSE          =     387.32

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .1882629   .0566729     3.32   0.001     .0768651    .2996607
                  |
          decades |
           1990s  |  -1032.737   66.86583   -15.44   0.000    -1164.171   -901.3041
           2000s  |  -1010.177   116.6012    -8.66   0.000    -1239.372   -780.9828
           2010s  |  -1218.931   2.586323  -471.30   0.000    -1224.015   -1213.847
                  |
            _cons |   2019.457    172.905    11.68   0.000      1679.59    2359.324
-----------------------------------------------------------------------------------

reg_idecades_23[6,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .18826289   .05667289   3.3219214   .00097221   .07686505   .29966073         420   1.9656283           0
  1b.decades           0           .           .           .           .           .         420   1.9656283           0
   2.decades  -1032.7374   66.865826  -15.444921   6.139e-43  -1164.1708  -901.30407         420   1.9656283           0
   3.decades  -1010.1775   116.60124  -8.6635229   9.976e-17  -1239.3722  -780.98278         420   1.9656283           0
   4.decades  -1218.9312   2.5863233  -471.29883           0  -1224.0149  -1213.8474         420   1.9656283           0
       _cons   2019.4573   172.90504   11.679575   1.743e-27   1679.5903   2359.3243         420   1.9656283           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_idecades_23.xlsx saved

Linear regression                               Number of obs     =        425
                                                F(2, 422)         =       6.88
                                                Prob > F          =     0.0011
                                                R-squared         =     0.0639
                                                Root MSE          =     389.53

-----------------------------------------------------------------------------------
                  |               Robust
wnc_offer_beg_m~y | Coefficient  std. err.      t    P>|t|     [95% conf. interval]
------------------+----------------------------------------------------------------
wcoal_end_monthly |   .2043997   .0567104     3.60   0.000     .0929296    .3158698
          decades |  -59.61963   108.8253    -0.55   0.584    -273.5268    154.2876
            _cons |   1077.866   207.2421     5.20   0.000      670.511    1485.222
-----------------------------------------------------------------------------------

reg_decades_23[3,9]
                       b          se           t      pvalue          ll          ul          df        crit       eform
wcoal_end_~y   .20439967   .05671044   3.6042691    .0003504   .09292956   .31586978         422   1.9656014           0
     decades  -59.619634   108.82533  -.54784703   .58408659  -273.52685   154.28758         422   1.9656014           0
       _cons   1077.8663    207.2421    5.201001   3.098e-07     670.511   1485.2217         422   1.9656014           0
Note: File will be replaced when the first putexcel command is issued.
file results/two/5_sample2_wnc_offer_reg_decades_23.xlsx saved

r; t=39.66 15:56:08

. 
. * (9.4) We are then able to do back of the envelope calculations for the expected 
. * value of continued employment, using the values estimated for rho (ex-mu), delta‚ lambda
. * and some additional assumptions:
. * - b = 0.5 wc
. * - deltaB = 0
. *
. **************************************************************************************
. *** (9.5) Elements for JAERE revision                                                                                      ***
. *** (9.5.1) Evolution of coal versus non-coal wages over time                                      ***
. *** (9.5.2) Distribution of first age of working in data                                                   ***
. *** (9.5.3) wage growth profiles of workers in diff. age ranges - by age ranges    ***
. **************************************************************************************
. 
. * NB. elements for JAERE revision also in section on transition 
. *     outcomes (sectoin ca. 2.1-2.4
. 
. *****************************************************************
. *** (9.5.1) Evolution of coal versus non-coal wages over time ***
. *****************************************************************
. 
. 
. * starting wages in non-coal / * last wages in coal by year
. forvalues i = 1975/$maxyear{
  2. g wc_`i'=.
  3. label var wc_`i' "last wage in coal if in year `i'"
  4. replace wc_`i'= wcoal_end_monthly if jahrend==`i' & wcoal_end_monthly <= $incmax
  5. NA
  6. 
. g wnc_`i'=.
  7. label var wnc_`i' "first wage in non-coal if in year `i'"
  8. replace wnc_`i'= wnc_offer_beg_monthly if jahrbeg==`i' & wnc_offer_beg_monthly <= $incmax
  9. count if jahrend==`i' & wnc_offer_beg_monthly > $incmax & wnc_offer_beg_monthly!=.
 10. * LH4.8.2022: how many observations removed due to our own criteria 
. 
. g wc_ca5_`i'=.
 11. replace wc_ca5_`i'= wcoal_end_monthly if (jahrend>=`i' - 5 & jahrend<`i' + 5) 
 12. g wc_ca10_`i'=.
 13. replace wc_ca10_`i'= wcoal_end_monthly if (jahrend>=`i' - 10 & jahrend<`i' + 10) 
 14. g wc_ca15_`i'=.
 15. replace wc_ca15_`i'= wcoal_end_monthly if (jahrend>=`i' - 15 & jahrend<`i' + 15) 
 16. 
. g wnc_ca5_`i'=.
 17. replace wnc_ca5_`i'= wnc_offer_beg_monthly if (jahrbeg>=`i' - 5 & jahrbeg<`i' + 5)
 18. g wnc_ca10_`i'=.
 19. replace wnc_ca10_`i'= wnc_offer_beg_monthly if (jahrbeg>=`i' - 10 & jahrbeg<`i' + 10)
 20. g wnc_ca15_`i'=.
 21. replace wnc_ca15_`i'= wnc_offer_beg_monthly if (jahrbeg>=`i' - 15 & jahrbeg<`i' + 15)
 22. 
. * yearly averages & standard deviation coal
. egen wc_mu_`i'=mean(wc_`i')
 23. label var wc_mu_`i' "mean last monthly wages in coal in `i'"
 24. egen wc_sd_`i'=sd(wc_`i')
 25. label var wc_sd_`i' "st dev of last monthly wages in coal in year `i'"
 26. 
. * yearly averages & standard deviation non-coal
. egen wnc_sd_`i'=sd(wnc_`i')
 27. label var wnc_sd_`i' "st dev of last monthly wages in ncoal in year `i'"
 28. egen wnc_mu_`i'=mean(wnc_`i')
 29. label var wnc_mu_`i' "mean offered monthly wages in non-coal in `i'"
 30. 
. 
. * moving averages: mean and standard deviation for 5 / 10 / 15 years
. egen wc_mav5_`i'=mean(wc_ca5_`i')
 31. label var wc_mav5_`i' "mean last wages in coal in `i' with 5 year-mov-average"
 32. egen wc_sd5_`i'=sd(wc_ca5_`i')
 33. label var wc_sd5_`i' "s.d. last wages in coal in `i' with 5 year-mov-average"
 34. egen wnc_mav5_`i'=mean(wnc_ca5_`i')
 35. label var wnc_mav5_`i' "mean offered wages in non-coal in `i' with 5 year-mov-average"
 36. egen wnc_sd5_`i'=sd(wnc_ca5_`i')
 37. label var wnc_sd5_`i' "s.d. offered wages in non-coal in `i' with 5 year-mov-average"
 38. 
. egen wc_mav10_`i'=mean(wc_ca10_`i')
 39. label var wc_mav10_`i' "mean last wages in coal in `i' with 10 year-mov-average"
 40. egen wc_sd10_`i'=sd(wc_ca10_`i')
 41. label var wc_sd10_`i' "s.d. last wages in coal in `i' with 10 year-mov-average"
 42. egen wnc_mav10_`i'=mean(wnc_ca10_`i')
 43. label var wnc_mav10_`i' "mean offered wages in non-coal in `i' with 10 year-mov-average"
 44. egen wnc_sd10_`i'=sd(wnc_ca10_`i')
 45. label var wnc_sd10_`i' "s.d. offered wages in non-coal in `i' with 10 year-mov-average"
 46. 
. egen wc_mav15_`i'=mean(wc_ca15_`i')
 47. label var wc_mav5_`i' "mean last wages in coal in `i' with 15 year-mov-average"
 48. egen wc_sd15_`i'=sd(wc_ca15_`i')
 49. label var wc_sd15_`i' "sd last wages in coal in `i' with 15 year-mov-average"
 50. egen wnc_mav15_`i'=mean(wnc_ca15_`i')
 51. label var wc_mav15_`i' "mean offered wages in non-coal in `i' with 15 year-mov-average"
 52. egen wnc_sd15_`i'=sd(wnc_ca15_`i')
 53. label var wnc_sd15_`i' "sd offered wages in non-coal in `i' with 15 year-mov-average"
 54. }

 (...) NA
 
 . forvalues i = 1975/$maxyear{
  2. g wdiff_mu_`i' = wc_mu_`i'[1] - wnc_mu_`i'[1]
  3. g wdiff_mav5_`i' = wc_mav5_`i'[1]-wnc_mav5_`i'[1]
  4. g wdiff_mav10_`i' = wc_mav10_`i'[1]-wnc_mav10_`i'[1]
  5. g wdiff_mav15_`i' = wc_mav15_`i'[1]-wnc_mav15_`i'[1]
  6. }
(1,027,536 missing values generated)
r; t=48.20 16:01:04

. su wdiff_mu*

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
wdiff_mu_~75 |          0
wdiff_mu_~76 |  1,027,536   -157.4515           0  -157.4515  -157.4515
wdiff_mu_~77 |  1,027,536   -456.9424           0  -456.9424  -456.9424
wdiff_mu_~78 |  1,027,536   -921.5854           0  -921.5854  -921.5854
wdiff_mu_~79 |  1,027,536   -334.0719           0  -334.0719  -334.0719
-------------+---------------------------------------------------------
wdiff_mu_~80 |  1,027,536    32.59399           0   32.59399   32.59399
wdiff_mu_~81 |  1,027,536    49.63696           0   49.63696   49.63696
wdiff_mu_~82 |  1,027,536    797.2163           0   797.2163   797.2163
wdiff_mu_~83 |  1,027,536    1066.717           0   1066.717   1066.717
wdiff_mu_~84 |  1,027,536   -1.786377           0  -1.786377  -1.786377
-------------+---------------------------------------------------------
wdiff_mu_~85 |  1,027,536    658.4436           0   658.4436   658.4436
wdiff_mu_~86 |  1,027,536    693.7302           0   693.7302   693.7302
wdiff_mu_~87 |  1,027,536    571.9744           0   571.9744   571.9744
wdiff_mu_~88 |  1,027,536    383.3562           0   383.3562   383.3562
wdiff_mu_~89 |  1,027,536   -105.5439           0  -105.5439  -105.5439
-------------+---------------------------------------------------------
wdiff_mu_~90 |  1,027,536    684.2092           0   684.2092   684.2092
wdiff_mu_~91 |  1,027,536     918.658           0    918.658    918.658
wdiff_mu_~92 |  1,027,536    347.1166           0   347.1166   347.1166
wdiff_mu_~93 |  1,027,536    743.7332           0   743.7332   743.7332
wdiff_mu_~94 |  1,027,536    929.6375           0   929.6375   929.6375
-------------+---------------------------------------------------------
wdiff_mu_~95 |  1,027,536    676.8451           0   676.8451   676.8451
wdiff_mu_~96 |  1,027,536    472.5686           0   472.5686   472.5686
wdiff_mu_~97 |  1,027,536    561.1176           0   561.1176   561.1176
wdiff_mu_~98 |  1,027,536    794.1055           0   794.1055   794.1055
wdiff_mu_~99 |  1,027,536    731.8473           0   731.8473   731.8473
-------------+---------------------------------------------------------
wdiff_mu_~00 |  1,027,536    366.3087           0   366.3087   366.3087
wdiff_mu_~01 |  1,027,536    398.8716           0   398.8716   398.8716
wdiff_mu_~02 |  1,027,536    928.3019           0   928.3019   928.3019
wdiff_mu_~03 |  1,027,536     738.783           0    738.783    738.783
wdiff_mu_~04 |  1,027,536    1239.373           0   1239.373   1239.373
-------------+---------------------------------------------------------
wdiff_mu_~05 |  1,027,536    522.3179           0   522.3179   522.3179
wdiff_mu_~06 |  1,027,536    818.5837           0   818.5837   818.5837
wdiff_mu_~07 |  1,027,536    428.5662           0   428.5662   428.5662
wdiff_mu_2~8 |  1,027,536    1008.651           0   1008.651   1008.651
wdiff_mu_2~9 |  1,027,536    669.4999           0   669.4999   669.4999
-------------+---------------------------------------------------------
wdiff_mu_~10 |  1,027,536    1073.521           0   1073.521   1073.521
wdiff_mu_~11 |  1,027,536    957.3553           0   957.3553   957.3553
wdiff_mu_~12 |  1,027,536    1432.241           0   1432.241   1432.241
wdiff_mu_~13 |  1,027,536    1112.241           0   1112.241   1112.241
wdiff_mu_~14 |  1,027,536   -63.56372           0  -63.56372  -63.56372
-------------+---------------------------------------------------------
wdiff_mu_~15 |  1,027,536     1536.44           0    1536.44    1536.44
wdiff_mu_~16 |  1,027,536     849.094           0    849.094    849.094
wdiff_mu_~17 |  1,027,536    1201.143           0   1201.143   1201.143
r; t=2.21 16:01:06

. su wdiff_mav5*

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
wdif~v5_1975 |  1,027,536   -537.3564           0  -537.3564  -537.3564
wdif~v5_1976 |  1,027,536   -284.8156           0  -284.8156  -284.8156
wdif~v5_1977 |  1,027,536   -182.8262           0  -182.8262  -182.8262
wdif~v5_1978 |  1,027,536    454.8013           0   454.8013   454.8013
wdif~v5_1979 |  1,027,536    569.3484           0   569.3484   569.3484
-------------+---------------------------------------------------------
wdif~v5_1980 |  1,027,536    531.3245           0   531.3245   531.3245
wdif~v5_1981 |  1,027,536    545.4673           0   545.4673   545.4673
wdif~v5_1982 |  1,027,536    564.3569           0   564.3569   564.3569
wdif~v5_1983 |  1,027,536    612.6606           0   612.6606   612.6606
wdif~v5_1984 |  1,027,536    621.1416           0   621.1416   621.1416
-------------+---------------------------------------------------------
wdif~v5_1985 |  1,027,536    629.4636           0   629.4636   629.4636
wdif~v5_1986 |  1,027,536     674.343           0    674.343    674.343
wdif~v5_1987 |  1,027,536    721.4727           0   721.4727   721.4727
wdif~v5_1988 |  1,027,536    368.9871           0   368.9871   368.9871
wdif~v5_1989 |  1,027,536    542.2332           0   542.2332   542.2332
-------------+---------------------------------------------------------
wdif~v5_1990 |  1,027,536    589.3859           0   589.3859   589.3859
wdif~v5_1991 |  1,027,536    602.2061           0   602.2061   602.2061
wdif~v5_1992 |  1,027,536    584.6407           0   584.6407   584.6407
wdif~v5_1993 |  1,027,536    584.0457           0   584.0457   584.0457
wdif~v5_1994 |  1,027,536     594.569           0    594.569    594.569
-------------+---------------------------------------------------------
wdif~v5_1995 |  1,027,536    600.8618           0   600.8618   600.8618
wdif~v5_1996 |  1,027,536    591.4825           0   591.4825   591.4825
wdif~v5_1997 |  1,027,536    587.6958           0   587.6958   587.6958
wdif~v5_1998 |  1,027,536    679.4937           0   679.4937   679.4937
wdif~v5_1999 |  1,027,536    644.3427           0   644.3427   644.3427
-------------+---------------------------------------------------------
wdif~v5_2000 |  1,027,536    617.3584           0   617.3584   617.3584
wdif~v5_2001 |  1,027,536     595.545           0    595.545    595.545
wdif~v5_2002 |  1,027,536    674.9802           0   674.9802   674.9802
wdif~v5_2003 |  1,027,536    709.1548           0   709.1548   709.1548
wdif~v5_2004 |  1,027,536      685.08           0     685.08     685.08
-------------+---------------------------------------------------------
wdif~v5_2005 |  1,027,536       672.6           0      672.6      672.6
wdif~v5_2006 |  1,027,536    822.2362           0   822.2362   822.2362
wdif~v5_2007 |  1,027,536    904.1658           0   904.1658   904.1658
wdif~v5_2008 |  1,027,536    907.0988           0   907.0988   907.0988
wdif~v5_2009 |  1,027,536    1019.438           0   1019.438   1019.438
-------------+---------------------------------------------------------
wdif~v5_2010 |  1,027,536    920.2577           0   920.2577   920.2577
wdif~v5_2011 |  1,027,536    1055.846           0   1055.846   1055.846
wdif~v5_2012 |  1,027,536    965.3923           0   965.3923   965.3923
wdif~v5_2013 |  1,027,536    1159.213           0   1159.213   1159.213
wdif~v5_2014 |  1,027,536    1162.895           0   1162.895   1162.895
-------------+---------------------------------------------------------
wdif~v5_2015 |  1,027,536    1166.218           0   1166.218   1166.218
wdif~v5_2016 |  1,027,536    1070.414           0   1070.414   1070.414
wdif~v5_2017 |  1,027,536    1038.019           0   1038.019   1038.019
r; t=2.30 16:01:08

. su wdiff_mav10*

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
wdiff~0_1975 |  1,027,536    531.3245           0   531.3245   531.3245
wdiff~0_1976 |  1,027,536    538.6707           0   538.6707   538.6707
wdiff~0_1977 |  1,027,536     545.928           0    545.928    545.928
wdiff~0_1978 |  1,027,536    545.7939           0   545.7939   545.7939
wdiff~0_1979 |  1,027,536    540.0417           0   540.0417   540.0417
-------------+---------------------------------------------------------
wdiff~0_1980 |  1,027,536    517.2009           0   517.2009   517.2009
wdiff~0_1981 |  1,027,536    523.7839           0   523.7839   523.7839
wdiff~0_1982 |  1,027,536    556.6941           0   556.6941   556.6941
wdiff~0_1983 |  1,027,536    384.3206           0   384.3206   384.3206
wdiff~0_1984 |  1,027,536    538.6702           0   538.6702   538.6702
-------------+---------------------------------------------------------
wdiff~0_1985 |  1,027,536    583.1696           0   583.1696   583.1696
wdiff~0_1986 |  1,027,536    596.7192           0   596.7192   596.7192
wdiff~0_1987 |  1,027,536    579.3821           0   579.3821   579.3821
wdiff~0_1988 |  1,027,536    578.7705           0   578.7705   578.7705
wdiff~0_1989 |  1,027,536     589.436           0    589.436    589.436
-------------+---------------------------------------------------------
wdiff~0_1990 |  1,027,536    594.8512           0   594.8512   594.8512
wdiff~0_1991 |  1,027,536    593.3428           0   593.3428   593.3428
wdiff~0_1992 |  1,027,536    592.2667           0   592.2667   592.2667
wdiff~0_1993 |  1,027,536    588.9513           0   588.9513   588.9513
wdiff~0_1994 |  1,027,536    590.7585           0   590.7585   590.7585
-------------+---------------------------------------------------------
wdiff~0_1995 |  1,027,536    600.6569           0   600.6569   600.6569
wdiff~0_1996 |  1,027,536    600.6522           0   600.6522   600.6522
wdiff~0_1997 |  1,027,536    603.0084           0   603.0084   603.0084
wdiff~0_1998 |  1,027,536     603.912           0    603.912    603.912
wdiff~0_1999 |  1,027,536    606.1406           0   606.1406   606.1406
-------------+---------------------------------------------------------
wdiff~0_2000 |  1,027,536    609.7632           0   609.7632   609.7632
wdiff~0_2001 |  1,027,536     611.952           0    611.952    611.952
wdiff~0_2002 |  1,027,536    611.3621           0   611.3621   611.3621
wdiff~0_2003 |  1,027,536    699.0237           0   699.0237   699.0237
wdiff~0_2004 |  1,027,536     679.507           0    679.507    679.507
-------------+---------------------------------------------------------
wdiff~0_2005 |  1,027,536    648.7322           0   648.7322   648.7322
wdiff~0_2006 |  1,027,536    641.3962           0   641.3962   641.3962
wdiff~0_2007 |  1,027,536    721.1721           0   721.1721   721.1721
wdiff~0_2008 |  1,027,536    775.1735           0   775.1735   775.1735
wdiff~0_2009 |  1,027,536    752.3944           0   752.3944   752.3944
-------------+---------------------------------------------------------
wdiff~0_2010 |  1,027,536     744.333           0    744.333    744.333
wdiff~0_2011 |  1,027,536     880.764           0    880.764    880.764
wdiff~0_2012 |  1,027,536    953.7151           0   953.7151   953.7151
wdiff~0_2013 |  1,027,536    945.7554           0   945.7554   945.7554
wdiff~0_2014 |  1,027,536    1019.128           0   1019.128   1019.128
-------------+---------------------------------------------------------
wdiff~0_2015 |  1,027,536    931.2715           0   931.2715   931.2715
wdiff~0_2016 |  1,027,536    1026.842           0   1026.842   1026.842
wdiff~0_2017 |  1,027,536    1013.988           0   1013.988   1013.988
r; t=2.28 16:01:11

. su wdiff_mav15*

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
wdif~15_1975 |  1,027,536    517.2009           0   517.2009   517.2009
wdif~15_1976 |  1,027,536    523.7839           0   523.7839   523.7839
wdif~15_1977 |  1,027,536    556.6941           0   556.6941   556.6941
wdif~15_1978 |  1,027,536    384.3206           0   384.3206   384.3206
wdif~15_1979 |  1,027,536    538.6702           0   538.6702   538.6702
-------------+---------------------------------------------------------
wdif~15_1980 |  1,027,536    583.1696           0   583.1696   583.1696
wdif~15_1981 |  1,027,536    595.6456           0   595.6456   595.6456
wdif~15_1982 |  1,027,536    577.9857           0   577.9857   577.9857
wdif~15_1983 |  1,027,536    576.2583           0   576.2583   576.2583
wdif~15_1984 |  1,027,536    585.7124           0   585.7124   585.7124
-------------+---------------------------------------------------------
wdif~15_1985 |  1,027,536    589.6726           0   589.6726   589.6726
wdif~15_1986 |  1,027,536    579.6743           0   579.6743   579.6743
wdif~15_1987 |  1,027,536    577.7386           0   577.7386   577.7386
wdif~15_1988 |  1,027,536    583.4243           0   583.4243   583.4243
wdif~15_1989 |  1,027,536    585.4979           0   585.4979   585.4979
-------------+---------------------------------------------------------
wdif~15_1990 |  1,027,536    592.1471           0   592.1471   592.1471
wdif~15_1991 |  1,027,536    592.5803           0   592.5803   592.5803
wdif~15_1992 |  1,027,536    594.7667           0   594.7667   594.7667
wdif~15_1993 |  1,027,536    595.8801           0   595.8801   595.8801
wdif~15_1994 |  1,027,536    598.5071           0   598.5071   598.5071
-------------+---------------------------------------------------------
wdif~15_1995 |  1,027,536    601.7397           0   601.7397   601.7397
wdif~15_1996 |  1,027,536    611.7712           0   611.7712   611.7712
wdif~15_1997 |  1,027,536    613.7935           0   613.7935   613.7935
wdif~15_1998 |  1,027,536    608.1567           0   608.1567   608.1567
wdif~15_1999 |  1,027,536    610.7826           0   610.7826   610.7826
-------------+---------------------------------------------------------
wdif~15_2000 |  1,027,536    616.4061           0   616.4061   616.4061
wdif~15_2001 |  1,027,536    618.2019           0   618.2019   618.2019
wdif~15_2002 |  1,027,536    619.1766           0   619.1766   619.1766
wdif~15_2003 |  1,027,536    622.4404           0   622.4404   622.4404
wdif~15_2004 |  1,027,536    623.1716           0   623.1716   623.1716
-------------+---------------------------------------------------------
wdif~15_2005 |  1,027,536    624.9226           0   624.9226   624.9226
wdif~15_2006 |  1,027,536    625.4978           0   625.4978   625.4978
wdif~15_2007 |  1,027,536    623.8837           0   623.8837   623.8837
wdif~15_2008 |  1,027,536    710.5209           0   710.5209   710.5209
wdif~15_2009 |  1,027,536    691.8146           0   691.8146   691.8146
-------------+---------------------------------------------------------
wdif~15_2010 |  1,027,536    656.6797           0   656.6797   656.6797
wdif~15_2011 |  1,027,536    647.0364           0   647.0364   647.0364
wdif~15_2012 |  1,027,536    729.2062           0   729.2062   729.2062
wdif~15_2013 |  1,027,536    775.1735           0   775.1735   775.1735
wdif~15_2014 |  1,027,536    752.3944           0   752.3944   752.3944
-------------+---------------------------------------------------------
wdif~15_2015 |  1,027,536     744.333           0    744.333    744.333
wdif~15_2016 |  1,027,536     880.764           0    880.764    880.764
wdif~15_2017 |  1,027,536    953.7151           0   953.7151   953.7151
r; t=2.57 16:01:13

. 
. 
. ************************************************************
. *** (9.5.2) Distribution of first age of working in data ***
. ************************************************************
. g agebeglig_P75=.
(1,027,536 missing values generated)
r; t=0.04 16:01:13

. replace agebeglig_P75=agebeg if jahrbeg>1975 & thisspelllignite==1
(285,223 real changes made)
r; t=0.06 16:01:13

. replace agebeglig_P75=. if (jahrbeg<=1975 | jahrbeg==.| thisspelllignite==0)
(0 real changes made)
r; t=0.07 16:01:14

. * given that we are trying to identify the age of recrutement, 
. * we exclude spells that already started pre-1975
. * by setting them to zero and then removing them - across all obs per person
. bys persnr: egen agestart=min(agebeglig_P75) 
(93,620 missing values generated)
r; t=1.70 16:01:15

. 
. * only use one observation per person for statistics on age distribution
. bys persnr: replace agestart=. if _n>1
(807735 real changes made, 807735 to missing)
r; t=0.06 16:01:15

. 
. su agestart, detail

                          agestart
-------------------------------------------------------------
      Percentiles      Smallest
 1%           18             15
 5%           18             15
10%           18             15       Obs             126,181
25%           24             15       Sum of wgt.     126,181

50%           35                      Mean           35.32197
                        Largest       Std. dev.      12.28062
75%           46             70
90%           53             70       Variance       150.8136
95%           54             74       Skewness       .1310067
99%           59             75       Kurtosis       1.802584
r; t=0.41 16:01:16

. tab agestart 

   agestart |      Freq.     Percent        Cum.
------------+-----------------------------------
         15 |         NA        0.00        0.00
         16 |         NA        0.03        0.04
         17 |        621        0.49        0.53
         18 |     14,068       11.15       11.68
         19 |      3,517        2.79       14.46
         20 |      2,896        2.30       16.76
         21 |      3,016        2.39       19.15
         22 |      2,607        2.07       21.22
         23 |      2,554        2.02       23.24
         24 |      2,581        2.05       25.29
         25 |      2,667        2.11       27.40
         26 |      2,781        2.20       29.60
         27 |      2,986        2.37       31.97
         28 |      3,113        2.47       34.44
         29 |      3,223        2.55       36.99
         30 |      3,150        2.50       39.49
         31 |      3,216        2.55       42.04
         32 |      3,075        2.44       44.47
         33 |      3,113        2.47       46.94
         34 |      3,012        2.39       49.33
         35 |      2,965        2.35       51.68
         36 |      3,011        2.39       54.06
         37 |      2,977        2.36       56.42
         38 |      2,859        2.27       58.69
         39 |      2,988        2.37       61.06
         40 |      3,122        2.47       63.53
         41 |      3,119        2.47       66.00
         42 |      3,137        2.49       68.49
         43 |      2,666        2.11       70.60
         44 |      2,390        1.89       72.50
         45 |      2,214        1.75       74.25
         46 |      1,782        1.41       75.66
         47 |      2,113        1.67       77.34
         48 |      2,843        2.25       79.59
         49 |      2,987        2.37       81.96
         50 |      2,980        2.36       84.32
         51 |      3,523        2.79       87.11
         52 |      3,591        2.85       89.96
         53 |      3,608        2.86       92.82
         54 |      3,440        2.73       95.54
         55 |      2,273        1.80       97.34
         56 |        814        0.65       97.99
         57 |        681        0.54       98.53
         58 |        547        0.43       98.96
         59 |        491        0.39       99.35
         60 |        319        0.25       99.60
         61 |        183        0.15       99.75
         62 |        123        0.10       99.85
         63 |        109        0.09       99.93
         64 |          NA       0.03       99.96
         65 |          NA       0.03       99.99
         66 |          NA       0.00       99.99
         67 |          NA       0.00       99.99
         68 |          NA       0.00      100.00
         69 |          NA       0.00      100.00
         70 |          NA       0.00      100.00
         74 |          NA       0.00      100.00
         75 |          NA       0.00      100.00
------------+-----------------------------------
      Total |    126,181      100.00
r; t=0.21 16:01:16

. 
. tab agestart if decades==3

   agestart |      Freq.     Percent        Cum.
------------+-----------------------------------
         16 |         NA        0.04        0.04
         17 |        124        0.42        0.46
         18 |      4,065       13.77       14.23
         19 |        880        2.98       17.21
         20 |        746        2.53       19.73
         21 |        734        2.49       22.22
         22 |        628        2.13       24.35
         23 |        549        1.86       26.21
         24 |        609        2.06       28.27
         25 |        655        2.22       30.49
         26 |        736        2.49       32.98
         27 |        821        2.78       35.76
         28 |        836        2.83       38.59
         29 |        868        2.94       41.53
         30 |        810        2.74       44.28
         31 |        802        2.72       46.99
         32 |        820        2.78       49.77
         33 |        819        2.77       52.54
         34 |        743        2.52       55.06
         35 |        755        2.56       57.62
         36 |        769        2.60       60.22
         37 |        760        2.57       62.80
         38 |        770        2.61       65.40
         39 |        758        2.57       67.97
         40 |        855        2.90       70.87
         41 |        950        3.22       74.09
         42 |        944        3.20       77.28
         43 |        776        2.63       79.91
         44 |        732        2.48       82.39
         45 |        679        2.30       84.69
         46 |        488        1.65       86.34
         47 |        530        1.80       88.14
         48 |        679        2.30       90.44
         49 |        665        2.25       92.69
         50 |        523        1.77       94.46
         51 |        534        1.81       96.27
         52 |        508        1.72       97.99
         53 |        187        0.63       98.62
         54 |        128        0.43       99.06
         55 |        102        0.35       99.40
         56 |          NA       0.18       99.58
         57 |          NA       0.13       99.71
         58 |          NA       0.09       99.80
         59 |          NA       0.08       99.88
         60 |          NA       0.05       99.94
         61 |          NA       0.02       99.96
         62 |          NA       0.01       99.97
         63 |          NA       0.01       99.98
         64 |          NA       0.01       99.99
         65 |          NA       0.00       99.99
         67 |          NA       0.00       99.99
         69 |          NA       0.00      100.00
         70 |          NA       0.00      100.00
------------+-----------------------------------
      Total |     29,524      100.00
r; t=0.29 16:01:16

. 
. *******************************************************************************************
. *** *** (9.5.3) wage growth profiles of workers in different age ranges - by age ranges ***
. *******************************************************************************************
. * the relevant variables are created in 1prepare,
. * since consecutive spells are collated there
. 
. su tentgrowlig

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
 tentgrowlig |    105,384    .1250258    .3141391  -.9995201   2.979297
r; t=0.04 16:01:16

. 
. ********************************************
. *** Differences in wage growth by decade ***
. ********************************************
. 
. su tentgrowlig

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
 tentgrowlig |    105,384    .1250258    .3141391  -.9995201   2.979297
r; t=0.05 16:01:16

. 
. su tentgrowlig if decades==1

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
 tentgrowlig |     28,661    .1474303    .3941418  -.9952833   2.979297
r; t=0.31 16:01:17

. 
. su tentgrowlig if decades==2

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
 tentgrowlig |     44,271     .121848    .2607224  -.9995201   2.974067
r; t=0.30 16:01:17

. 
. su tentgrowlig if decades==3

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
 tentgrowlig |     18,847    .1315242    .2719129   -.995904      2.833
r; t=0.29 16:01:17

. 
. su tentgrowlig if decades==4

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
 tentgrowlig |     13,605     .079166    .3320929  -.9983274   2.973387
r; t=0.27 16:01:17

. 
. 
. ***********************************************
. *** Differences in wage growth by age range ***
. ***********************************************
. 
. su tentgrowlig_twen 

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
tentgrowli~n |    222,206    .1428245    .1673677  -.9872968   2.932333
r; t=0.05 16:01:18

.         *"wage growth in lignite if aged up to 30"
. su tentgrowlig_thir 

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
tentgrowl~ir |     93,814    .0863271    .1451095  -.9989992   2.618241
r; t=0.05 16:01:18

.         *"wage growth in lignite if aged 30-40"
. su tentgrowlig_four 

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
tentgrowl~ur |     88,951    .0721253    .1404776  -.9957578   2.924121
r; t=0.04 16:01:18

.         *"wage growth in lignite if aged 40-50"
. su tentgrowlig_five 

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
tentgrowli~e |     92,731    .0544448    .1462863  -.9927198   2.983955
r; t=0.05 16:01:18

.         *"wage growth in lignite if aged 50-60"
. su tentgrowlig_sixp 

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
tentgrowli~p |      4,742    .0383973    .2104483  -.9560115   2.495412
r; t=0.05 16:01:18

.         *"wage growth in lignite if aged 60plus"
. 
. * spells ending post 2010 only: *
. su tentgrowlig_twen if decades==4

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
tentgrowli~n |     34,286    .0942882    .1322531  -.8674775   2.373541
r; t=0.30 16:01:18

.         *"wage growth in lignite if aged up to 30"
. su tentgrowlig_thir if decades==4

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
tentgrowl~ir |      8,006    .0616456    .1579502  -.7700258   2.309752
r; t=0.32 16:01:18

.         *"wage growth in lignite if aged 30-40"
. su tentgrowlig_four if decades==4

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
tentgrowl~ur |      7,267    .0434315    .1573655  -.9338257   1.843442
r; t=0.27 16:01:19

.         *"wage growth in lignite if aged 40-50"
. su tentgrowlig_five if decades==4

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
tentgrowli~e |      9,284    .0318017     .174343  -.9396502   2.983955
r; t=0.28 16:01:19

.         *"wage growth in lignite if aged 50-60"
. su tentgrowlig_sixp if decades==4

    Variable |        Obs        Mean    Std. dev.       Min        Max
-------------+---------------------------------------------------------
tentgrowli~p |      1,473    .0233168    .2175446  -.8251246   2.495412
r; t=0.30 16:01:19

.         *"wage growth in lignite if aged 60plus"
. 
.         
.         
. cap log close
